Get 20M+ Full-Text Papers For Less Than $1.50/day. Subscribe now for You or Your Team.

Learn More →

Body mass index and survival in women with breast cancer—systematic literature review and meta-analysis of 82 follow-up studies

Body mass index and survival in women with breast cancer—systematic literature review and... Annals of Oncology reviews Annals of Oncology 25: 1901–1914, 2014 doi:10.1093/annonc/mdu042 Published online 27 April 2014 Body mass index and survival in women with breast cancer—systematic literature review and meta-analysis of 82 follow-up studies 1 1 1,2 3 4 5 D. S. M. Chan , A. R. Vieira , D. Aune , E. V. Bandera , D. C. Greenwood , A. McTiernan , 1 6,7 8 1 D. Navarro Rosenblatt , I. Thune , R. Vieira & T. Norat 1 2 Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; Department of Public Health and General Practice, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway; Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New 4 5 Jersey, New Jersey, USA; Division of Biostatistics, Centre for Epidemiology and Biostatistics, University of Leeds, Leeds, UK; Division of Public Health Sciences, Fred 6 7 Hutchinson Cancer Research Center, Washington, USA; Department of Oncology, Oslo University Hospital, Oslo; Faculty of Health Sciences, Department of Community Medicine, University of Tromso, Tromso, Norway; School of Mathematics and Statistics, University of Newcastle, Newcastle upon Tyne, UK Received 12 December 2013; accepted 16 January 2014 Background: Positive association between obesity and survival after breast cancer was demonstrated in previous meta-analyses of published data, but only the results for the comparison of obese versus non-obese was summarised. Methods: We systematically searched in MEDLINE and EMBASE for follow-up studies of breast cancer survivors with body mass index (BMI) before and after diagnosis, and total and cause-specific mortality until June 2013, as part of the World Cancer Research Fund Continuous Update Project. Random-effects meta-analyses were conducted to explore the magnitude and the shape of the associations. Results: Eighty-two studies, including 213 075 breast cancer survivors with 41 477 deaths (23 182 from breast cancer) were identified. For BMI before diagnosis, compared with normal weight women, the summary relative risks (RRs) of total mortality were 1.41 [95% confidence interval (CI) 1.29–1.53] for obese (BMI >30.0), 1.07 (95 CI 1.02–1.12) for overweight (BMI 25.0–<30.0) and 1.10 (95% CI 0.92–1.31) for underweight (BMI <18.5) women. For obese women, the summary RRs were 1.75 (95% CI 1.26–2.41) for pre-menopausal and 1.34 (95% CI 1.18–1.53) for post-menopausal breast cancer. For each 5 kg/m increment of BMI before, <12 months after, and ≥12 months after diagnosis, increased risks of 17%, 11%, and 8% for total mortality, and 18%, 14%, and 29% for breast cancer mortality were observed, respectively. Conclusions: Obesity is associated with poorer overall and breast cancer survival in pre- and post-menopausal breast cancer, regardless of when BMI is ascertained. Being overweight is also related to a higher risk of mortality. Randomised clinical trials are needed to test interventions for weight loss and maintenance on survival in women with breast cancer. Key words: body mass index, meta-analysis, survival after breast cancer, systematic literature review obesity [4], which has further been linked to breast cancer recur- introduction rence [5] and poorer survival in pre- and post-menopausal The number of female breast cancer survivors is growing because breast cancer [6, 7]. Preliminary findings from randomised, con- of longer survival as a consequence of advances in treatment and trolled trials suggest that lifestyle modifications improved bio- early diagnosis. There were ∼2.6 million female breast cancer sur- markers associated with breast cancer progression and overall vivors in US in 2008 [1], and in the UK, breast cancer accounted survival [8]. for ∼28% of the 2 million cancer survivors in 2008 [2]. The biological mechanisms underlying the association between Obesity is a pandemic health concern, with over 500 million obesity and breast cancer survival are not established, and could adults worldwide estimated to be obese and 958 million were involve interacting mediators of hormones, adipocytokines, and overweight in 2008 [3]. One of the established risk factors for inflammatory cytokines which link to cell survival or apoptosis, breast cancer development in post-menopausal women is migration, and proliferation [9]. Higher level of oestradiol pro- duced in postmenopausal women through aromatisation of androgens in the adipose tissues [10], and higher level of insulin *Correspondence to: Doris S. M. Chan, Department of Epidemiology and Biostatistics, [11], a condition common in obese women, are linked to poorer School of Public Health, Imperial College London, St Mary’s Campus, Norfolk Place, prognosis in breast cancer. A possible interaction between leptin London W2 1PG, UK. Tel: +44-0-20-759-48590; Fax: +44-0-20-759-43193; and insulin [12], and obesity-related markers of inflammation E-mail: d.chan@imperial.ac.uk © The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Annals of Oncology reviews [13] have also been linked to breast cancer outcomes. Non-bio- statistical analysis logical mechanisms could include chemotherapy under-dosing in Categorical and dose–response meta-analyses were conducted using random-effects models to account for between-study het- obese women, suboptimal treatment, and obesity-related compli- cations [14]. erogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each Numerous studies have examined the relationship between obesity and breast cancer outcomes, and past reviews have con- study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component cluded that obesity is linked to a lower survival; however, when investigated in a meta-analysis of published data, only the results which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used of obese compared with non-obese or lighter women were summarised [6, 7, 15]. for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also We carried out a systematic literature review and meta-ana- lysis of published studies to explore the magnitude and the included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet’s Index (QI) measured in shape of the association between body fatness, as measured by body mass index (BMI), and the risk of total and cause-specific units of kg/m was used. We conducted categorical meta-analyses by pooling the cat- mortality, overall and in women with pre- and post-menopausal breast cancer. As body weight may change close to diagnosis egorical results reported in the studies. The studies used differ- ent BMI categories. In some studies, underweight (BMI <18.5 and during primary treatment of breast cancer [16], we exam- ined BMI in three periods: before diagnosis, <12 months after kg/m according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m ) were classified diagnosis, and ≥12 months after breast cancer diagnosis. together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 2 2 materials and methods kg/m ) and obese (BMI ≥30.0 kg/m ) women separately but, in some studies, overweight and obese women were combined. The data sources and search reference category was normal weight or underweight together We carried out a systematic literature search, limited to publica- with normal weight, depending on the studies. For convenience, tions in English, for articles on BMI and survival in women with the BMI categories are referred to as underweight, normal breast cancer in OVID MEDLINE and EMBASE from inception weight, overweight, and obese in the present review. We derived to 30 June 2013 using the search strategy implemented for the the RRs for overweight and obese women compared with WCRF/AICR Continuous Update Project on breast cancer sur- normal weight women in two studies [19, 21] that had more vival. The search strategy contained medical subject headings and than four BMI categories using the method of Hamling et al. text words that covered a broad range of factors on diet, physical [22]. Studies that reported results for obese compared with non- activity, and anthropometry. The protocol for the review is obese women were analysed separately. available at http://www.dietandcancerreport.org/index.php [17]. The non-linear dose–response relationship between BMI and In addition, we hand-searched the reference lists of relevant arti- mortality was examined using the best-fitting second-order frac- cles, reviews, and meta-analysis papers. tional polynomial regression model [23], defined as the one with the lowest deviance. Non- linearity was tested using the likelihood ratio test [24]. In the study selection non-linear meta-analysis, the reference category was the lowest Included were follow-up studies of breast cancer survivors, BMI category in each study and RRs were recalculated using the which reported estimates of the associations of BMI ascertained method of Hamling et al. [22] when the reference category was before and after breast cancer diagnosis with total or cause- not the lowest BMI category in the study. specific mortality risks. Studies that investigated BMI after diag- We also conducted linear dose–response meta-analyses, ex- nosis were divided into two groups: BMI <12 months after diag- cluding the category underweight when reported separately in nosis (BMI <12 months) and BMI 12 months or more after the studies, by pooling estimates of RR per unit increase (with diagnosis (BMI ≥12 months). Outcomes included total mortal- its standard error) provided by the studies, or derived by us ity, breast cancer mortality, death from cardiovascular disease, from categorical data using generalised least-squares for trend and death from causes other than breast cancer. When multiple estimation [25]. To estimate the trend, the numbers of publications on the same study population were found, results outcomes and population at-risk for at least three BMI categor- based on longer follow-up and more outcomes were selected for ies, or the information required to derive them using standard the meta-analysis. methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the cat- data extraction egories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended DSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related infor- category to estimate the midpoints. Where the RRs were pre- sented by subgroups (age group [27], menopausal status [28, mation, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and popula- 29], stage [30] or subtype [31] of breast cancer, or others [32– 34]), an overall estimate for the study was obtained by a fixed- tion at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m of BMI. and adjustment factors in the analysis.  | Chan et al. Volume 25 | No. 10 | October 2014 Annals of Oncology reviews To assess heterogeneity, we computed the Cochran Q test and total, breast cancer or non-breast cancer mortality in obese I statistic [35]. The cut points of 30% and 50% were used for women (before or <12 months after diagnosis) compared with low, moderate, and substantial level of heterogeneity. Sources of the reference BMI [69, 71–74, 76, 77, 79, 82], two publications heterogeneity were explored by meta-regression and subgroup reported non-significant inverse associations [75, 80] and three analyses using pre-defined factors, including indicators of study publications reported no association [70, 78, 81] of BMI with quality (menopausal status, hormone receptor status, number of survival after breast cancer. Hence, 79 publications from 82 outcomes, length of follow-up, study design, geographic loca- follow-up studies with 41 477 deaths (23 182 from breast tion, BMI assessment, adjustment for confounders, and others). cancer) in 213 075 breast cancer survivors were included in the Small study or publication bias was examined by Egger’s test meta-analyses (Figure 1). Supplementary Table S1, available at [36] and visual inspection of the funnel plots. The influence of Annals of Oncology online shows the characteristics of the each individual study on the summary RR was examined by ex- studies included in the meta-analyses and details of the excluded cluding the study in turn [37]. A P value of <0.05 was consid- studies are in supplementary Table S2, available at Annals of ered statistically significant. All analyses were conducted using Oncology online. Results of the meta-analyses are summarised Stata version 12.1 (Stata Statistical Software: Release 12, in Table 1. StataCorp LP, College Station, TX). Studies were follow-up of women with breast cancer iden- tified in prospective aetiologic cohort studies (women were free of cancer at enrolment), or cohorts of breast cancer survivors results whose participants were identified in hospitals or through A total of 124 publications investigating the relationship of body cancer registries, or follow-up of breast cancer patients enrolled in case–control studies or randomised clinical trials. fatness and mortality in women with breast cancer were iden- tified. We excluded 31 publications, including four publications Some studies included only premenopausal women [83–85] or postmenopausal women [21, 27, 86–94], but most studies on other obesity indices [38–41], 12 publications without a measure of association [42–53], and 15 publications superseded included both. Menopausal status was usually determined at time of diagnosis. Year of diagnosis was from 1957–1965 [70]to by publications of the same study with more outcomes [54–68]. A further 14 publications were excluded because of insufficient 2002–2009 [74]. Patient tumour characteristics and stage of disease at diagnosis varied across studies, and some studies data for the meta-analysis (five publications [69–73]) or un- adjusted results (nine publications [74–82]), from which nine included carcinoma in situ. No all studies provided clinical in- formation on the tumour, treatment, and co-morbidities. publications reported statistically significant increased risk of 21 566 records excluded on the basis of title 22 590 unique records identified in MEDLINE and EMBASE until 30 June 2013 and abstract 19 articles found in handsearch 651 articles excluded for not fulfilling the inclusion criteria 87 no original data 338 did not report on the associations of 1043 full-text articles retrieved and assessed interest for inclusion 33 abstract/commentary 10 meta-analyses 183 irrelevant study design 268 articles did not investigate body 392 potentially relevant articles in women fatness and mortality with breast cancer 31 articles excluded in present review 4 examined obesity index 12 no measure of association 124 articles on body fatness and mortality 15 superseded publications 14 articles excluded in meta-analysis 5 insufficient data 8 unadjusted results 79 relevant articles (82 studies) on body mass index and mortality included in the meta-analyses in present review Figure 1. Flowchart of search. Volume 25 | No. 10 | October 2014 doi:10.1093/annonc/mdu042 |  Annals of Oncology reviews  | Chan et al. Volume 25 | No. 10 | October 2014 Table 1. Summary of meta-analyses of BMI and survival in women with breast cancer BMI before diagnosis BMI <12 months after diagnosis BMI ≥12 months after diagnosis 2 2 2 N RR (95% CI) I (%) N RR (95% CI) I (%) N RR (95% CI) I (%) P P P h h h Total mortality Under versus normal weight 10 1.10 (0.92–1.31) 48% 11 1.25 (0.99–0.57) 63% 3 1.29 (1.02–1.63) 0% 0.04 <0.01 0.39 Over versus normal weight 19 1.07 (1.02–1.12) 0% 22 1.07 (1.02–1.12) 21% 4 0.98 (0.86–1.11) 0% 0.88 0.18 0.72 Obese versus normal weight 21 1.41 (1.29–1.53) 38% 24 1.23 (1.12–1.33) 69% 5 1.21 (1.06–1.38) 0% 0.04 <0.01 0.70 Obese versus non-obese –– – 12 1.26 (1.07–1.47) 80% –– – <0.01 Per 5 kg/m increase 15 1.17 (1.13–1.21) 7% 12 1.11 (1.06–1.16) 55% 4 1.08 (1.01–1.15) 0% 0.38 0.01 0.52 Breast cancer mortality Under versus normal weight 8 1.02 (0.85–1.21) 31% 5 1.53 (1.27–1.83) 0% 1 1.10 (0.15–8.08) – 0.18 0.59 Over versus normal weight 21 1.11 (1.06–1.17) 0% 12 1.11 (1.03–1.20) 14% 2 1.37 (0.96–1.95) 0% 0.66 0.31 0.90 Obese versus normal weight 22 1.35 (1.24–1.47) 36% 12 1.25 (1.10–1.42) 53% 2 1.68 (0.90–3.15) 67% 0.05 0.02 0.08 Obese versus non-obese –– – 6 1.26 (1.05–1.51) 64% –– – 0.02 Per 5 kg/m increase 18 1.18 (1.12–1.25) 47% 8 1.14 (1.05–1.24) 66% 2 1.29 (0.97–1.72) 64% 0.01 0.01 0.10 Cardiovascular disease related mortality Over versus normal weight 2 1.01 (0.80–1.29) 0% –– – – – – 0.87 Obese versus normal weight 2 1.60 (0.66–3.87) 78% –– – – – – 0.03 Per 5 kg/m increase 2 1.21 (0.83–1.77) 80% –– – – – – 0.03 Non-breast cancer mortality Over versus normal weight –– – 5 0.96 (0.83–1.11) 26% –– – 0.25 Obese versus normal weight –– – 5 1.29 (0.99–1.68) 72% –– – 0.01 BMI before and after diagnosis (<12 months after, or ≥12 months after diagnosis) was classified according to the exposure period which the studies referred to in the BMI assessment; the BMI categories were included in the categorical meta-analyses as defined by the studies. P , P for heterogeneity between studies. h Annals of Oncology reviews % BMI Weight kg/m Study RR (95% Cl) Underweight v normal weight Buck 2011 1.98 (0.79, 4.96) 3.29 <18.5 v 18.5–24.9 Conroy 2011 1.13 (0.89, 1.42) 17.23 <22.5 v 22.5–24.9 Lu 2011 0.89 (0.70, 1.14) 16.79 <20 v 20–24.9 Chen 2010 1.44 (0.88, 2.37) 8.51 <=18.4 v 18.5–24.9 Emaus 2010 1.70 (0.86, 3.33) 5.44 <=18.4 v 18.5–24.9 < Hellmann 2010 1.36 (0.87, 2.11) 9.79 <=19.9 v 20–25 Nichols 2009 1.75 (0.94, 3.25) 6.21 <=18.4 v 18.5–24.9 Abrahamson 2006 0.73 (0.52, 1.04) 12.75 <=18.4 v 18.5–24.9 Kroenke 2005 0.89 (0.70, 1.13) 16.98 <21 v 21–22 Bernstein 2002 1.13 (0.43, 2.97) 3.01 <=18.4 v 18.5–24.9 Subtotal (I-squared = 48.2%, P = 0.043) 1.10 (0.92, 1.31) 100.00 Overweight v normal weight Kamineni 2013 1.09 (0.69, 1.72) 1.18 25–29.9 v <25 Buck 2011 1.03 (0.69, 1.56) 1.47 25–29.9 v 18.5–24.9 Conroy 2011 1.15 (0.93, 1.42) 5.47 25–29.9 v 22.5–24.9 Lu 2011 0.99 (0.84, 1.15) 9.94 25–29.9 v 20–24.9 Chen 2010 1.02 (0.81, 1.27) 4.85 25–29.9 v 18.5–24.9 Emaus 2010 1.02 (0.81, 1.27) 4.85 25–29.9 v 18.5–24.9 Hellmann 2010 1.22 (0.92, 1.61) 3.13 25.1–30 v 20–25 Keegan 2010 1.16 (0.92, 1.45) 4.74 25–29.9 v <=24.9 Nichols 2009 1.13 (0.90, 1.42) 4.71 25–29.9 v 18.5–24.9 West-Wright 2009 0.98 (0.78, 1.24) 4.56 25–29 v <=24 Caan 2008 1.20 (0.80, 1.70) 1.73 25–29.9 v <=24.9 Dal Maso 2008 1.02 (0.83, 1.25) 5.85 25–29.9 v <=24.9 Reding 2008 1.20 (0.90, 1.60) 2.96 >22.4–25.8 v <–20.6 Reeves 2007 1.02 (0.94, 1.12) 32.20 25–<30 v 18.5–<25 Abrahamson 2006 1.47 (0.96, 2.24) 1.37 25–29.9 v 18.5–24.9 Kroenke 2005 1.11 (0.91, 1.34) 6.55 25–29 v 21–22 Reeves 2000 1.19 (0.92, 1.53) 3.79 25–26 v <=24 Zhang 1995 1.00 (0.50, 2.20) 0.45 24.7–28.8 v 16–24.6 Holmberg 1994 2.38 (0.84, 6.77) 0.23 25–28 v <19 Subtotal (I-squared = 0.0%, P = 0.882) 1.07 (1.02, 1.12) 100.00 Obese v normal weight Kamineni 2013 1.31 (0.77, 2.22) 2.20 >=30 v <25 Buck 2011 1.15 (0.54, 2.46) 1.17 >=30 v 18.5–24.9 Conroy 2011 1.54 (1.23, 1.91) 7.31 >=30 v 22.5–24.9 Lu 2011 1.23 (1.04, 1.47) 8.97 >=30 v 20–24.9 Chen 2010 1.58 (1.13, 2.22) 4.42 >=30 v 18.5–24.9 Emaus 2010 1.47 (1.08, 1.99) 5.05 >=30 v 18.5–24.9 Hellmann 2010 1.61 (1.12, 2.33) 3.94 >30 v 20–25 Keegan 2010 1.21 (1.00, 1.48) 8.12 >=30 v <=24.9 Nichols 2009 1.52 (1.17, 1.98) 6.06 >=30 v 18.5–24.9 West-Wright 2009 1.42 (1.08, 1.88) 5.70 >=30 v <25 Caan 2008 1.60 (1.10, 2.30) 3.90 >=30 v <=24.9 Dal Maso 2008 1.29 (0.99, 1.68) 6.02 >=30 v <=24.9 Reding 2008 1.90 (1.40, 2.50) 5.39 >=25.8 v <=20.6 Cleveland 2007 1.63 (1.08, 2.45) 3.34 >30 v <24.9 Reeves 2007 1.06 (0.86, 1.30) 7.67 >=30 v 18.5–25 Abrahamson 2006 2.93 (1.37, 6.29) 1.16 >=30 v 18.5–24.9 Kroenke 2005 1.20 (0.95, 1.52) 6.85 >=30 v 21–22 Bernstein 2002 1.18 (0.81, 1.72) 3.78 >25 v 18.5–24.9 Reeves 2000 1.49 (1.18, 1.86) 7.08 >=27 v <=24 Zhang 1995 1.50 (0.70, 2.90) 1.31 28.9–45.9 v 16–24.6 Holmberg 1994 5.93 (1.98, 17.80) 0.58 >=29 v <19 Subtotal (I-squared = 37.6%, P = 0.043) 1.41 (1.29, 1.53) 100.00 0.125 1 8 Figure 2. Categorical meta-analysis of pre-diagnosis BMI and total mortality. Most of the studies were based in North America or Europe. 24 698 patients [97]. Total number of deaths ranged from 56 There were three studies from each of Australia [79, 95, 96], [93] to 7397 [108], and the proportion of deaths from breast Korea [97, 98] and China [99–101]; two studies from Japan [71, cancer ranged from 22% [27] to 98% [84] when reported. All 102]; one study from Tunisia [103] and four international but eight studies [30, 93, 94, 98, 99, 109–111] had an average studies [19, 104–106]. Study size ranged from 96 [107]to follow-up of more than 5 years. Volume 25 | No. 10 | October 2014 doi:10.1093/annonc/mdu042 |  Annals of Oncology reviews 2 2 BMI and total mortality women (I = 69%, P < 0.01; I = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 categorical meta-analysis. For BMI before diagnosis, compared (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI with normal weight women, the summary RRs were 1.41 (95% 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02– 1.02–1.63, 3 studies) for underweight women (supplementary 1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92– Figure S2, available at Annals of Oncology online). Twelve 1.31, 10 studies) for underweight women (Figure 2). For BMI additional studies reported results for obese versus non-obese <12 months after diagnosis and the same comparisons, the women <12 months after diagnosis, and the summary RR was summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for 1.26 (95% CI 1.07–1.47, I = 80%, P < 0.01). obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at dose–response meta-analysis. There was evidence of a J-shaped Annals of Oncology online). Substantial heterogeneities were association in the non-linear dose–response meta-analyses of observed between studies of obese women and underweight BMI before and after diagnosis with total mortality (all P < 0.01; Total mortality Breast cancer mortality Pre-diagnosis BMI Pre-diagnosis BMI 2 3 1.5 Best fitting fractional polynomial Best fitting fractional polynomial P < 0.001 P = 0.21 95% confidence interval 95% confidence interval 0.5 0.5 15 20 25 30 35 40 15 20 25 30 35 40 BMI (kg/m ) 2 BMI (kg/m ) BMI <12 months after diagnosis BMI <12 months after diagnosis Best fitting fractional polynomial Best fitting fractional polynomial 95% confidence interval 95% confidence interval P = 0.007 P = 1.00 0.5 0.5 15 20 25 30 35 40 15 20 25 30 35 40 BMI (kg/m ) BMI (kg/m ) BMI >12 months after diagnosis BMI >12 months after diagnosis Best fitting fractional polynomial Best fitting fractional polynomial 95% confidence interval 95% confidence interval 1.5 1 1.5 P < 0.001 P = 0.86 0.5 0.5 15 20 25 30 35 40 15 20 25 30 35 40 BMI (kg/m ) BMI (kg/m ) Figure 3. Non-linear dose–response curves of BMI and mortality.  | Chan et al. Volume 25 | No. 10 | October 2014 RR RR RR RR RR RR Annals of Oncology reviews Per 5 kg/m % Study BMI RR (95% Cl) Weight Pre-diagnosis BMI Kamineni 2013 1.14 (0.88, 1.47) 1.62 Conroy 2011 1.28 (1.14, 1.46) 6.95 Lu 2011 1.09 (1.00, 1.19) 13.36 Chen 2010 1.15 (1.01, 1.32) 5.79 Emaus 2010 1.14 (1.00, 1.30) 6.15 Hellmann 2010 1.26 (1.05, 1.52) 3.21 Nichols 2009 1.20 (1.06, 1.35) 7.26 West-Wright 2009 1.15 (1.01, 1.31) 5.89 Caan 2008 1.26 (1.05, 1.52) 3.12 Dal Maso 2008 1.11 (0.98, 1.26) 6.61 Reding 2008 1.17 (1.10, 1.23) 25.25 Abrahamson 2006 1.52 (1.16, 1.99) 1.49 Kroenke 2005 1.13 (1.02, 1.25) 9.06 Zhang 1995 1.14 (0.93, 1.39) 2.59 Helmberg 1994 1.47 (1.14, 1.89) 1.67 Subtotal (I-squared = 6.6%, P = 0.379) 1.17 (1.13, 1.21) 100.00 BMI <12 months after diagnosis Ewertz 2012 1.08 (0.99, 1.19) 10.73 Goodwin 2012 1.12 (0.94, 1.34) 4.97 Kawai 2012 1.52 (0.89, 2.60) 0.71 Baumgartner 2011 0.94 (0.83, 1.06) 7.91 Azambuja 2010 1.17 (1.06, 1.29) 10.00 Chen 2010 1.13 (0.99, 1.29) 7.31 Dawood 2008 1.12 (0.96, 1.30) 6.30 Majed 2008 1.05 (1.01, 1.10) 16.49 Vitolins 2008 1.22 (1.10, 1.34) 10.03 Abrahamson 2006 1.27 (1.11, 1.45) 7.20 Tao 2006 1.30 (1.01, 1.68) 2.78 Berclaz 2004 1.07 (1.02, 1.12) 15.58 Subtotal (I-squared = 54.8%, P = 0.011) 1.11 (1.06, 1.16) 100.00 BMI >=12 months after diagnosis Elatt 2010 1.11 (0.98, 1.27) 28.42 Nichols 2009 1.10 (0.98, 1.24) 35.22 Caan 2008 1.14 (0.92, 1.42) 10.34 Ewertz 1991 0.99 (0.86, 1.13) 26.01 Subtotal (I-squared = 0.0%, P = 0.517) 1.08 (1.01, 1.15) 100.00 0.5 1 2 Figure 4. Linear dose–response meta-analysis of BMI and total mortality. Figure 3), suggesting that underweight women may be at BMI and breast cancer mortality slightly increased risk compared with normal weight women. categorical meta-analysis. BMI was significantly associated The curves show linear increasing trends from 20 kg/m for with breast cancer mortality. Compared with normal weight BMI before diagnosis and <12 months after diagnosis, and from women, for BMI before diagnosis, the summary RRs were 1.35 25 kg/m for BMI ≥12 months after diagnosis. When linear (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI models were fitted excluding the underweight category, the 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI summary RRs of total mortality for each 5 kg/m increase in 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI after, and ≥12 months after diagnosis, respectively (Figure 4). 1.27–1.83, 5 studies) for underweight women (supplementary Substantial heterogeneity was observed between studies on BMI Figure S3, available at Annals of Oncology online). Substantial <12 months after diagnosis (I = 55%, P = 0.01). heterogeneity was observed between studies of obese women Volume 25 | No. 10 | October 2014 doi:10.1093/annonc/mdu042 |  Annals of Oncology reviews % BMI Weight kg/m Study RR (95% Cl) Underweight v normal weight Alsaker 2011 0.66 (0.38, 1.16) 8.17 <20 v 20–24.9 Conroy 2011 1.22 (0.87, 1.71) 16.83 <22.5 v 22.5–24.9 Lu 2011 0.86 (0.65, 1.12) 21.45 <20 v 20–24.9 Emaus 2010 1.49 (0.66, 3.37) 4.25 <18.5 v 18.5–25 Hellmann 2010 1.77 (0.99, 3.18) 7.60 <=19.9 v 20–25 Nichols 2009 0.93 (0.22, 3.85) 1.48 <=18.4 v 18.5–24.9 Kroenke 2005 0.88 (0.65, 1.19) 19.17 <21 v 21–22 Whiteman 2005 1.07 (0.81, 1.41) 21.05 <=18.5 v 18.51–22.90 Subtotal (I-squared = 31.1%, P = 0.179) 1.02 (0.85, 1.21) 100.00 Overweight v normal weight Kamineni 2013 1.45 (0.62, 3.39) 0.39 25–29.9 v <25 Alsaker 2011 1.14 (0.96, 1.35) 9.70 25–29.9 v 20–24.9 Conroy 2011 1.17 (0.86, 1.58) 3.05 25–29.9 v 22.5–24.9 Lu 2011 0.99 (0.83, 1.18) 9.10 25–29.9 v 20–24.9 Emaus 2010 1.01 (0.79, 1.30) 4.54 25.1–29.9 v 18.5–25 Hellmann 2010 1.23 (0.84, 1.79) 1.97 25.1–30 v 20–25 Nichols 2009 1.48 (0.98, 2.24) 1.65 25–29.9 v 18.5–24.9 Rosenberg 2009 0.90 (0.70, 1.20) 3.88 25–30 v <=24.9 West-Wright 2009 1.16 (0.83, 1.62) 2.52 25–29 v <=24 Caan 2008 1.40 (0.80, 2.30) 1.01 25–29.9 v <=24.9 Dal Maso 2008 1.07 (0.85, 1.35) 5.27 25–29.9 v <=24.9 Reeves 2007 1.05 (0.88, 1.26) 8.74 25–<30 v 18.5–<25 Kroenke 2005 1.12 (0.88, 1.43) 4.78 25–29 v 21–22 Whiteman 2005 1.25 (1.08, 1.44) 13.62 25–29.9 v <=22.9 Enger 2004 0.89 (0.63, 1.26) 2.35 22–24.8 v <20.4 Maehle 2004 1.03 (0.87, 1.40) 4.98 Q2-Q4 v Q1 Schairer 1999 1.30 (0.90, 1.70) 2.79 23.34–26.15 v <=21.28 Galanis 1998 1.70 (0.60, 4.50) 0.28 22.7–25.7 v <=22.6 Jain 1994 1.20 (0.75, 1.91) 1.29 24.14–27.34 v <=22.21 Tornberg 1993 1.40 (1.00, 1.90) 2.74 26–27 v <=21 Tretli 1990 1.09 (0.95, 1.24) 15.38 Q4 v Q1 Subtotal (I-squared = 0.0%, P = 0.658) 1.11 (1.06, 1.17) 100.00 Obese v normal weight Kamineni 2013 2.41 (1.00, 5.81) 0.88 >=30 v <25 Alsaker 2011 1.52 (1.25, 1.85) 8.27 >=30 v 20–24 Conroy 2011 1.45 (1.05, 2.00) 4.73 >=30 v 22.5–24.9 Lu 2011 1.20 (0.99, 1.46) 8.34 >=30 v 20–24.9 Emaus 2010 1.43 (1.01, 2.02) 4.28 >=30 v 18.5–25 Hellmann 2010 1.82 (1.11, 2.99) 2.46 >30 v 20–25 1.42 (0.86, 2.36) >=30 v 18.5–24.9 Nichols 2009 2.38 Rosenberg 2009 1.20 (0.90, 1.60) 5.49 >30 v <25 >=30 v <25 West-Wright 2009 1.71 (1.16, 2.53) 3.60 Caan 2008 1.60 (0.90, 2.70) 2.06 >=30 v <=24.9 >=30 v <=24.9 Dal Maso 2008 1.38 (1.02, 1.86) 5.19 Cleveland 2007 1.88 (1.04, 3.34) 1.86 >30 v <24.9 >=30 v 18.5–<25 Reeves 2007 1.12 (0.73, 1.73) 3.03 Kroenke 2005 1.09 (0.80, 1.48) 5.04 >=30 v 21–22 >=30 v <=22.9 Whiteman 2005 1.34 (1.09, 1.65) 7.86 Enger 2004 0.76 (0.53, 1.07) 4.20 >=24.9 v <20.4 Q5 v Q1 Maehle 2004 1.38 (1.04, 1.84) 5.55 Schairer 1999 1.60 (1.20, 2.10) 5.68 >=26.15 v <=21.28 >=25.8 v <=22.6 Galanis 1998 2.20 (0.90, 5.40) 0.85 Jain 1994 0.78 (0.48, 1.22) 2.71 >27.34 v <22.22 >=28 v <=21 Tornberg 1993 1.70 (1.20, 2.30) 4.67 Tretli 1990 1.35 (1.18, 1.54) 10.87 Q5 v Q1 Subtotal (I-squared = 35.5%, P = 0.051) 1.35 (1.24, 1.47) 100.00 0.125 1 8 Figure 5. Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality. (I = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the dose–response meta-analysis. There was no significant evidence summary RRs of the two studies identified were 1.68 (95% CI of a non-linear relationship between BMI before, <12 months 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for after, and ≥12 months after diagnosis and breast cancer overweight women (supplementary Figure S4, available at mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). Annals of Oncology online). The summary of another six studies When linear models were fitted excluding data from the that reported RRs for obese versus non-obese <12 months after underweight category, statistically significant increased risks of diagnosis was 1.26 (95% CI 1.05–1.51, I = 64%, P = 0.02). breast cancer mortality with BMI before and <12 months after  | Chan et al. Volume 25 | No. 10 | October 2014 Annals of Oncology reviews Per 5 kg/m % BMI RR (95% Cl) Weight Study Pre-diagnosis BMI Kamineni 2013 1.55 (1.00, 2.40) 1.43 Alsaker 2011 1.23 (1.12, 1.36) 9.57 Conroy 2011 1.24 (1.03, 1.48) 5.65 Lu 2011 1.08 (0.98, 1.19) 9.78 Hellmann 2010 1.33 (1.04, 1.70) 3.76 Nichols 2009 1.22 (0.97, 1.53) 4.20 Rosenberg 2009 1.07 (0.93, 1.23) 7.40 West-Wright 2009 1.28 (1.06, 1.55) 5.31 Caan 2008 1.27 (0.96, 1.67) 3.15 Dal Maso 2008 1.15 (1.00, 1.33) 7.23 Cleveland 2007 1.40 (1.09, 1.81) 3.50 Kroenke 2005 1.05 (0.92, 1.21) 7.54 Whiteman 2005 1.17 (1.07, 1.27) 10.33 Enger 2004 0.85 (0.66, 1.09) 3.64 Schairer 1999 1.36 (1.14, 1.62) 5.69 Galanis 1998 1.61 (1.02, 2.54) 1.34 Jain 1994 0.95 (0.78, 1.16) 4.86 Tornberg 1993 1.38 (1.15, 1.65) 5.64 Subtotal (I-squared = 47.3%, P = 0.014) 1.18 (1.12, 1.25) 100.00 BMI <12 months after diagnosis Hou 2013 0.98 (0.92, 1.05) 20.45 Kawai 2012 1.48 (0.80, 2.74) 1.76 Ranagopoulou 2012 1.13 (1.00, 1.27) 15.77 Sestak 2010 1.15 (1.05, 1.26) 18.09 Olsson 2009 1.11 (0.97, 1.26) 14.83 Vitolins 2008 1.22 (1.08, 1.36) 16.24 Hebert 1998 1.34 (1.01, 1.78) 6.43 Newman 1997 1.34 (1.01, 1.78) 6.44 Subtotal (I-squared = 65.8%, P = 0.005) 1.14 (1.05, 1.24) 100.00 BMI >=12 months after diagnosis Nichols 2009 1.48 (1.19, 1.83) 54.22 Caan 2008 1.10 (0.83, 1.44) 45.78 Subtotal (I-squared = 63.7%, P = 0.097) 1.29 (0.97, 1.72) 100.00 0.5 1 2 Figure 6. Linear dose–response meta-analysis of BMI and breast cancer mortality. diagnosis were observed (Figure 6). The summary RRs for each normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 2 2 5 kg/m increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 5 kg/m increase in BMI, the summary RR was 1.21 (95% CI breast cancer deaths) for BMI before diagnosis and 1.14 (95% 0.83–1.77). Five studies reported results for deaths from any cause CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. 2 2 months after diagnosis, with moderate (I = 47%, P = 0.01) and The summary RRs were 1.29 (95% CI 0.99–1.68, I =72%, 2 2 substantial (I = 66%, P = 0.01) heterogeneities between studies, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I =26%, respectively. Only two studies on BMI ≥12 months after P = 0.25) for overweight women compared with normal weight diagnosis and breast cancer mortality (N = 220 deaths) were women. identified. The summary RR was 1.29 (95% CI 0.97–1.72). BMI and other mortality outcomes subgroup, meta-regression, and sensitivity analyses Only two studies reported results for death from cardiovascular disease (N =151 deaths) [27, 112]. The summary RR for The results of the subgroup and meta-regression analyses are in obese versus normal weight before diagnosis was 1.60 (95% CI supplementary Tables S3 and S4, available at Annals of 0.66–3.87). No association was observed for overweight versus Oncology online. Subgroup analysis was not carried out for BMI Volume 25 | No. 10 | October 2014 doi:10.1093/annonc/mdu042 |  Annals of Oncology reviews ≥12 months after diagnosis as the limited number of studies associations observed in the linear dose–response meta-analysis. would hinder any meaningful comparisons. All associations were statistically significant, apart from the rela- Increased risks of mortality were observed in the meta-ana- tionship between BMI ≥12 months after diagnosis and breast lyses by menopausal status. While the summary risk estimates cancer mortality. This may be due to limited statistical power, seem stronger with premenopausal breast cancer, there was no with only 220 breast cancer deaths from two follow-up studies. significant heterogeneity between pre- and post-menopausal Positive associations, in some cases statistically significant, were breast cancer as shown in the meta-regression analyses also observed in overweight, and underweight women compared (P = 0.28–0.89) (supplementary Tables S3 and S4, available at with normal weight women. Women with BMI of 20 kg/m Annals of Oncology online). For BMI before diagnosis and total before, or <12 months after diagnosis, and of 25 kg/m mortality, the summary RRs for obese versus normal weight 12 months or more after diagnosis appeared to have the lowest were 1.75 (95% CI 1.26–2.41, I = 70%, P < 0.01, 7 studies) in mortality risk in the non-linear dose–response analysis. Co- women with pre-menopausal breast cancer and 1.34 (95% CI morbid conditions may cause the observed increased risk in 1.18–1.53, I = 27%, P = 0.20, 9 studies) in women with post- underweight women. Thorough investigation within the group menopausal breast cancer. and on their contribution to the shape of the association is hin- Studies with larger number of deaths [105, 115], conducted in dered, as not all studies in this review reported results for this Europe [28, 115], or with weight and height assessed through group. The increased risk associated with obesity was similar in medical records [28, 104, 115, 116] tended to report weaker pre- or post-menopausal breast cancer. We did not find any associations for BMI <12 months after diagnosis and total mor- evidence of a protective effect of obesity on survival after pre- tality compared with other studies (meta-regression P = 0.01, menopausal breast cancer, contrary to what has been 0.02, 0.01, respectively) (supplementary Table S3, available at observed for the development of breast cancer in pre-menopausal Annals of Oncology online); while studies with larger number of women [4]. deaths [101], conducted in Asia [101, 102], or adjusted for A large body of evidence with 41 477 deaths (23 182 from co-morbidity [101, 102] reported weaker associations for BMI breast cancer) in over 210 000 breast cancer survivors was sys- <12 months after diagnosis and breast cancer mortality (meta- tematically reviewed in the present study. We carried out cat- regression P = 0.01, 0.02, 0.01, respectively) (supplementary egorical, linear, and non-linear dose–response meta-analyses to Table S4, available at Annals of Oncology online). examine the magnitude and the shape of the associations for Analyses stratified by study designs, or restricted to studies total and cause-specific mortality in underweight, overweight, with invasive cases only, early-stage non-metastatic cases only, and obese women by time periods before and after diagnosis or mammography screening detected cases only, or controlled that is important in relation to the population-at-risk and breast for previous diseases did not produce results that were material- cancer survivors. Our findings agree with and further extend the ly different from those obtained in the overall analyses (results results from previous meta-analyses. A review published in 2010 not shown). Summary risk estimates remained statistically sign- reported statistically significant increased risks of 33% of both ificant when each study was omitted in turn, except for BMI total and breast cancer mortality for obesity versus non-obesity ≥12 months after diagnosis and total mortality. The summary around diagnosis [7]. These estimates are slightly higher than RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m increase when ours, which may be explained by the different search periods Flatt et al. [117] which contributed 315 deaths was omitted. and inclusion criteria for the articles (33 studies and 15 studies included in the analyses, respectively). Another review pub- lished in 2012 further reported consistent positive associations small studies or publication bias of total and breast cancer mortality with higher versus lower Asymmetry was only detected in the funnel plots of BMI <12 BMI around diagnosis [6]. No significant differences were months after diagnosis and total mortality, and breast cancer observed by menopausal status or hormone receptor status. The mortality, which suggests that small studies with an inverse After Breast Cancer Pooling Project of four prospective cohort association are missing (plots not shown). Egger’s tests studies found differential effects of levels of pre-diagnosis were borderline significant (P = 0.05) or statistically significant obesity on survival [118]. Compared with normal weight (P = 0.03), respectively. women, significant or borderline significant increased risks of 81% of total and 40% of breast cancer mortality were only observed for morbidly obese (≥40 kg/m ) women and not for discussion women in other obesity categories. We observed statistically The present systematic literature review and meta-analysis of significant increased risks also for overweight women, probably follow-up studies clearly supports that, in breast cancer survi- because of a larger number of studies. We were unable to investi- vors, higher BMI is consistently associated with lower overall gate the associations with severely and morbidly obese women and breast cancer survival, regardless of when BMI is ascer- because only two studies included in this review reported such tained. The limited number of studies on death from cardiovas- results [19, 113]. Overall, our findings are consistent with previ- cular disease is also consistent with a positive association. For ous meta-analyses in showing elevated total and breast cancer before, <12 months after, and 12 months or more after breast mortality associated with higher BMI and support the current cancer diagnosis, compared with normal weight women, obese guidelines for breast cancer survivors to stay as lean as possible women had 41%, 23%, and 21% higher risk for total mortality, within the normal range of body weight [4], for overweight and 35%, 25%, and 68% increased risk for breast cancer mortal- women to avoid weight gain during treatment and for obese ity, respectively. The findings were supported by the positive women to lose weight after treatment [119].  | Chan et al. Volume 25 | No. 10 | October 2014 Annals of Oncology reviews The present review is limited by the challenges and flaws co-morbidities, it is prudent to maintain a healthy body weight encountered by the individual epidemiological studies evaluating (BMI 18.5–<25.0 kg/m ) throughout life. the body fatness–mortality relationship in breast cancer survivors. Most studies did not adjust for co-morbidities and assess inten- acknowledgements tional weight loss. Women with more serious health issues, and especially smokers, may lose weight but are at an increased risk of TN is the principal investigator of the Continuous Update mortality, and this might cause an apparent increased risk in Project at Imperial College London. TN and DSMC wrote the underweight women. Body weight information through the protocol and implemented the study with the advice of an natural history of the disease and treatment information were expert committee convened by WCRF. RV developed and usually not complete or available. Increase of body weight post- managed the database for the Continuous Update Project. diagnosis is common in women with breast cancer, particularly DSMC, TN, and DA did the literature search and study selec- during chemotherapy [16]. Chemotherapy under-dosing is a tions. DSMC, ARV, and DNR did the data extraction. DSMC common problem in obese women and may contribute to their carried out the statistical analyses. DCG was statistical adviser increased mortality [120]. Although several studies with pre-diag- and contributed to the statistical analyses. DSMC wrote the first nosis BMI adjusted for underlying illnesses or excluded the first draft of the original manuscript. EB, AM, and IT are panel few years of follow-up, reverse causation may have affected the members of the Continuous Update Project and advised on the results in studies that assessed BMI in women with cancer and interpretation of the review. All authors revised the manuscript. other illnesses. However, in these studies, the associations were DSMC takes responsibility for the integrity of the data and the similar to other studies. Possible survival benefit (subjects with accuracy of the data analysis. better prognostic factors survive) may be present in the survival cohorts, in which the range of BMI could be narrower, and may cause an underestimation of the association. funding Follow-up studies with variable characteristics were pooled in This work was supported by the World Cancer Research Fund the meta-analysis. Women identified in clinical trials may have International (grant number: 2007/SP01) (http://www.wcrf-uk. had specific tumour subtypes, with fewer co-morbidities, and org/). The funder of this study had no role in the decisions were more likely to receive protocol treatments with high treat- about the design and conduct of the study; collection, manage- ment completion rates. Women who were recruited through ment, analysis, or interpretation of the data; or the preparation, mammography screening programmes may have had healthier review, or approval of the manuscript. The views expressed lifestyles or access to medical facilities, and more likely to be in this review are the opinions of the authors. They may not diagnosed with in situ or early-stage breast cancer. Cancer de- represent the views of the World Cancer Research Fund tection methods, tumour classifications and treatment regimens International/American Institute for Cancer Research and may change over time, and may vary within (if follow-up is long) and differ from those in future updates of the evidence related to between studies, and could not be simply examined by using the food, nutrition, physical activity, and cancer survival. diagnosis or treatment date. We cannot rule out the effect of un- measured or residual confounding in our analysis. Nevertheless, most results were adjusted for multiple confounding factors, in- disclosure cluding tumour stage or other-related variables and stratified analyses by several key factors showed similar summary risk DCG reports personal fees from World Cancer Research Fund/ American Institute for Cancer Research, during the conduct of estimates. Small study or publication bias was observed in the analyses of BMI the study; grants from Danone, and grants from Kelloggs, outside the submitted work. AM reports personal fees from <12 months after diagnosis. However, the overall evidence is supported by large, well-designed studies and is unlikely to be Metagenics/Metaproteomics, personal fees from Pfizer, outside the submitted work. All remaining authors have declared no changed. We did not conduct analyses by race/ethnicity and treatment types as only limited studies had published results. conflicts of interest. Future studies of body fatness and breast cancer outcomes should aim to account for co-morbidities, separate intended and references unintended changes of body weight, and collect complete treat- ment information during study follow-up. Randomised clinical 1. American Cancer Society. Breast Cancer Facts & Figures 2011–2012. Atlanta: trials are needed to test interventions for weight loss and main- American Cancer Society, Inc. 2012. tenance on survival in women with breast cancer. 2. Maddams J, Brewster D, Gavin A et al. Cancer prevalence in the United Kingdom: estimates for 2008. Br J Cancer 2009; 101: 541–547. In conclusion, the present systematic literature review and 3. Finucane MM, Stevens GA, Cowan MJ et al. National, regional, and global trends meta-analysis extends and confirms the associations of obesity in body-mass index since 1980: systematic analysis of health examination with an unfavourable overall and breast cancer survival in pre- surveys and epidemiological studies with 960 country-years and 9.1 million and post-menopausal breast cancer, regardless of when BMI is participants. Lancet 2011; 377: 557–567. ascertained. Increased risks of mortality in underweight and 4. World Cancer Research Fund/American Institute for Cancer Research. Food, overweight women were also observed. Given the comparable Nutrition, Physical Activity, and the Prevention of Cancer: a Global Perspective. elevated risks with obesity in the development (for post- Washington DC: AICR 2007. menopausal women) and prognosis of breast cancer, and the 5. Ligibel J. Obesity and breast cancer. Oncology (Williston Park) 2011; 25: complications with cancer treatment and other obesity-related 994–1000. Volume 25 | No. 10 | October 2014 doi:10.1093/annonc/mdu042 |  Annals of Oncology reviews 6. Niraula S, Ocana A, Ennis M et al. Body size and breast cancer prognosis in 30. Tretli S, Haldorsen T, Ottestad L. The effect of pre-morbid height and weight on relation to hormone receptor and menopausal status: a meta-analysis. Breast the survival of breast cancer patients. Br J Cancer 1990; 62: 299–303. Cancer Res Treat 2012; 134: 769–781. 31. Sparano JA, Wang M, Zhao F et al. Obesity at diagnosis is associated with 7. Protani M, Coory M, Martin JH. Effect of obesity on survival of women with breast inferior outcomes in hormone receptor-positive operable breast cancer. Cancer cancer: systematic review and meta-analysis. Breast Cancer Res Treat 2010; 2012; 118: 5937–5946. 123: 627–635. 32. Lu Y, Ma H, Malone KE et al. Obesity and survival among black women and white 8. Pekmezi DW, Demark-Wahnefried W. Updated evidence in support of diet and women 35 to 64 years of age at diagnosis with invasive breast cancer. J Clin exercise interventions in cancer survivors. Acta Oncol 2011; 50: 167–178. Oncol 2011; 29: 3358–3365. 9. Hursting SD, Berger NA. Energy balance, host-related factors, and cancer 33. Olsson A, Garne JP, Tengrup I et al. Body mass index and breast cancer survival progression. J Clin Oncol 2010; 28: 4058–4065. in relation to the introduction of mammographic screening. Eur J Surg Oncol 2009; 35: 1261–1267. 10. Lonning PE. Aromatase inhibition for breast cancer treatment. Acta Oncol 1996; 35(Suppl 5): 38–43. 34. Connor AE, Baumgartner RN, Pinkston C et al. Obesity and risk of breast cancer mortality in Hispanic and non-Hispanic white women: the New Mexico Women’s 11. Goodwin PJ, Ennis M, Bahl M et al. High insulin levels in newly diagnosed breast Health Study. J Women’s Health 2013; 22: 368–377. cancer patients reflect underlying insulin resistance and are associated with components of the insulin resistance syndrome. Breast Cancer Res Treat 2009; 35. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat 114: 517–525. Med 2002; 21: 1539–1558. 12. Goodwin PJ, Ennis M, Fantus IG et al. Is leptin a mediator of adverse prognostic 36. Sterne JA, Gavaghan D, Egger M. Publication and related bias in meta-analysis: effects of obesity in breast cancer? J Clin Oncol 2005; 23: 6037–6042. power of statistical tests and prevalence in the literature. J Clin Epidemiol 2000; 53: 1119–1129. 13. Pierce BL, Ballard-Barbash R, Bernstein L et al. Elevated biomarkers of inflammation are associated with reduced survival among breast cancer patients. 37. Tobias A. Assessing the influence of a single study in meta-analysis. Stata Tech J Clin Oncol 2009; 27: 3437–3444. Bull 1999; 47: 15–17. 14. Greenman CG, Jagielski CH, Griggs JJ. Breast cancer adjuvant chemotherapy 38. Abe R, Kumagai N, Kimura M et al. Biological characteristics of breast cancer in dosing in obese patients: dissemination of information from clinical trials to obesity. Tohoku J Exp Med 1976; 120: 351–359. clinical practice. Cancer 2008; 112: 2159–2165. 39. Bastarrachea J, Hortobagyi GN, Smith TL et al. Obesity as an adverse prognostic 15. Ryu SY, Kim CB, Nam CM et al. Is body mass index the prognostic factor in factor for patients receiving adjuvant chemotherapy for breast cancer. Ann Intern breast cancer? A meta-analysis. J Korean Med Sci 2001; 16: 610–614. Med 1994; 120: 18–25. 16. Demark-Wahnefried W, Campbell KL, Hayes SC. Weight management and its 40. Donegan WL, Jayich S, Koehler MR. The prognostic implications of obesity for role in breast cancer rehabilitation. Cancer 2012; 118: 2277–2287. the surgical cure of breast cancer. Breast 1978; 4: 14–17. 17. World Cancer Research Fund/American Institute for Cancer Research: 41. Nomura AM, Marchand LL, Kolonel LN et al. The effect of dietary fat on breast Continuous Update Project (CUP). 2013. cancer survival among Caucasian and japanese women in Hawaii. Breast Cancer Res Treat 1991; 18(Suppl. 1): S135–S141. 18. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986; 7: 177–188. 42. Albain KS, Green S, LeBlanc M et al. Proportional hazards and recursive partitioning and amalgamation analyses of the Southwest Oncology Group node- 19. de Azambuja E, Caskill-Stevens W, Francis P et al. The effect of body mass index positive adjuvant CMFVP breast cancer data base: a pilot study. Breast Cancer on overall and disease-free survival in node-positive breast cancer patients treated Res Treat 1992; 22: 273–284. with docetaxel and doxorubicin-containing adjuvant chemotherapy: the experience of the BIG 02–98 trial. Breast Cancer Res Treat 2010; 119: 145–153. 43. Bergmann A, Bourrus NS, de Carvalho CM et al. Arm symptoms and overall survival in Brazilian patients with advanced breast cancer. Asian Pac J Cancer 20. Vitolins MZ, Kimmick GG, Case LD. BMI influences prognosis following surgery Prev 2011; 12: 2939–2942. and adjuvant chemotherapy for lymph node positive breast cancer. Breast J 2008; 14: 357–365. 44. Coates RJ, Clark WS, Eley JW et al. Race, nutritional status, and survival from breast cancer. J Natl Cancer Inst 1990; 82: 1684–1692. 21. Sestak I, Distler W, Forbes JF et al. Effect of body mass index on recurrences in tamoxifen and anastrozole treated women: an exploratory analysis from the ATAC 45. Crujeiras AB, Cueva J, Vieito M et al. Association of breast cancer and obesity in trial. J Clin Oncol 2010; 28: 3411–3415. a homogeneous population from Spain. J Endocrinol Invest 2012; 35: 681–685. 22. Hamling J, Lee P, Weitkunat R et al. Facilitating meta-analyses by deriving 46. Kimura M. Obesity as prognostic factors in breast cancer. Diabetes Res Clin Pract relative effect and precision estimates for alternative comparisons from a set of 1990; 10: S247–S251. estimates presented by exposure level or disease category. Stat Med 2008; 27: 47. Lara-Medina F, Perez-Sanchez V, Saavedra-Perez D et al. Triple-negative breast 954–970. cancer in Hispanic patients: high prevalence, poor prognosis, and association 23. Royston P, Ambler G, Sauerbrei W. The use of fractional polynomials to model with menopausal status, body mass index, and parity. Cancer 2011; 117: continuous risk variables in epidemiology. Int J Epidemiol 1999; 28: 964–974. 3658–3669. 24. Bagnardi V, Zambon A, Quatto P et al. Flexible meta-regression functions for 48. Lethaby AE, Mason BH, Harvey VJ et al. Survival of women with node negative modeling aggregate dose-response data, with an application to alcohol and breast cancer in the Auckland region. N Z Med J 1996; 109: 330–333. mortality. Am J Epidemiol 2004; 159: 1077–1086. 49. Sendur MAN, Aksoy S, Zengin N et al. Efficacy of adjuvant aromatase inhibitor in 25. Orsini N, Bellocco R, Greenland S. Generalized least squares for trend estimation hormone receptor-positive postmenopausal breast cancer patients according to of summarized dose-response data. Stata J 2006; 6: 40–57. the body mass index. Br J Cancer 2012; 107: 1815–1819. 26. Bekkering GE, Harris RJ, Thomas S et al. How much of the data published in 50. Singh AK, Pandey A, Tewari M et al. Obesity augmented breast cancer risk: a observational studies of the association between diet and prostate or bladder potential risk factor for Indian women. J Surg Oncol 2011; 103: 217–222. cancer is usable for meta-analysis? Am J Epidemiol 2008; 167: 1017–1026. 51. Taylor SG, Knuiman MW, Sleeper LA et al. Six-year results of the Eastern 27. Reeves KW, Faulkner K, Modugno F et al. Body mass index and mortality among Cooperative Oncology Group trial of observation versus CMFP versus CMFPT in older breast cancer survivors in the Study of Osteoporotic Fractures. Cancer postmenopausal patients with node-positive breast cancer. J Clin Oncol 1989; 7: Epidemiol Biomarkers Prev 2007; 16: 1468–1473. 879–889. 28. Baumgartner AK, Hausler A, Seifert-Klauss V et al. Breast cancer after hormone 52. Loehberg CR, Almstedt K, Jud SM et al. Prognostic relevance of Ki-67 in the replacement therapy—does prognosis differ in perimenopausal and primary tumor for survival after a diagnosis of distant metastasis. Breast Cancer postmenopausal women? Breast 2011; 20: 448–454. Res Treat 2013; 138: 899–908. 29. Cleveland RJ, Eng SM, Abrahamson PE et al. Weight gain prior to diagnosis and 53. Mousa U, Onur H, Utkan G. Is obesity always a risk factor for all breast cancer survival from breast cancer. Cancer Epidemiol Biomarkers Prev 2007; 16: patients? c-erbB2 expression is significantly lower in obese patients with early 1803–1811. stage breast cancer. Clin Transl Oncol 2012; 14: 923–930.  | Chan et al. Volume 25 | No. 10 | October 2014 Annals of Oncology reviews 54. Anderson SJ, Wapnir I, Dignam JJ et al. Prognosis after ipsilateral breast tumor 78. Menon KV, Hodge A, Houghton J et al. Body mass index, height and cumulative recurrence and locoregional recurrences in patients treated by breast-conserving menstrual cycles at the time of diagnosis are not risk factors for poor outcome in therapy in five National Surgical Adjuvant Breast and Bowel Project protocols of breast cancer. Breast 1999; 8: 328–333. node-negative breast cancer. J Clin Oncol 2009; 27: 2466–2473. 79. Rohan TE, Hiller JE, McMichael AJ. Dietary factors and survival from breast 55. Bayraktar S, Hernadez-Aya LF, Lei X et al. Effect of metformin on survival cancer. Nutr Cancer 1993; 20: 167–177. outcomes in diabetic patients with triple receptor-negative breast cancer. Cancer 80. Saxe GA, Rock CL, Wicha MS et al. Diet and risk for breast cancer recurrence 2012; 118: 1202–1211. and survival. Breast Cancer Res Treat 1999; 53: 241–253. 56. Daling JR, Malone KE, Doody DR et al. Relation of body mass index to tumor 81. Schuetz F, Diel IJ, Pueschel M et al. Reduced incidence of distant markers and survival among young women with invasive ductal breast metastases and lower mortality in 1072 patients with breast cancer with a carcinoma. Cancer 2001; 92: 720–729. history of hormone replacement therapy. Am J Obstet Gynecol 2007; 196: 57. Eralp Y, Smith TL, Altundag K et al. Clinical features associated with a favorable 342–349. outcome following neoadjuvant chemotherapy in women with localized breast 82. Tammemagi CM, Nerenz D, Neslund-Dudas C et al. Comorbidity and survival cancer aged 35 years or younger. J Cancer Res Clin Oncol 2009; 135: disparities among black and white patients with breast cancer. JAMA 2005; 141–148. 294: 1765–1772. 58. Ewertz M. Breast cancer in Denmark. Incidence, risk factors, and characteristics 83. Enger SM, Bernstein L. Exercise activity, body size and premenopausal breast of survival. Acta Oncol 1993; 32: 595–615. cancer survival. Br J Cancer 2004; 90: 2138–2141. 59. Ganz PA, Habel LA, Weltzien EK et al. Examining the influence of beta blockers 84. Holmberg L, Lund E, Bergstrom R et al. Oral contraceptives and prognosis in and ACE inhibitors on the risk for breast cancer recurrence: results from the breast cancer: effects of duration, latency, recency, age at first use and relation LACE cohort. Breast Cancer Res Treat 2011; 129: 549–556. to parity and body mass index in young women with breast cancer. Eur J Cancer 60. Goodwin PJ, Ennis M, Pritchard KI et al. Fasting insulin and outcome in early- 1994; 30A: 351–354. stage breast cancer: results of a prospective cohort study. J Clin Oncol 2002; 85. Reding KW, Daling JR, Doody DR et al. Effect of prediagnostic alcohol 20: 42–51. consumption on survival after breast cancer in young women. Cancer Epidemiol 61. Greenberg ER, Vessey MP, McPherson K et al. Body size and survival in Biomarkers Prev 2008; 17: 1988–1996. premenopausal breast cancer. Br J Cancer 1985; 51: 691–697. 86. Alsaker MDK, Opdahl S, Asvold BO et al. The association of reproductive factors 62. Holmes MD, Stampfer MJ, Colditz GA et al. Dietary factors and the survival of and breastfeeding with long term survival from breast cancer. Breast Cancer Res women with breast carcinoma.[Erratum appears in Cancer 1999 Dec 15;86 Treat 2011; 130: 175–182. (12):2707–8]. Cancer 1999; 86: 826–835. 87. Buck K, Vrieling A, Zaineddin AK et al. Serum enterolactone and prognosis of 63. Jain M, Miller AB. Tumor characteristics and survival of breast cancer patients in postmenopausal breast cancer. J Clin Oncol 2011; 29: 3730–3738. relation to premorbid diet and body size. Breast Cancer Res Treat 1997; 42: 88. Clough-Gorr KM, Ganz PA, Silliman RA. Older breast cancer survivors: factors 43–55. associated with self-reported symptoms of persistent lymphedema over 7 years 64. Jung SY, Sereika SM, Linkov F et al. The effect of delays in treatment for breast of follow-up. Breast J 2010; 16: 147–155. cancer metastasis on survival. Breast Cancer Res Treat 2011; 130: 953–964. 89. Conroy SM, Maskarinec G, Wilkens LR et al. Obesity and breast cancer survival in 65. Maehle BO, Tretli S. Pre-morbid body-mass-index in breast cancer: reversed ethnically diverse postmenopausal women: the Multiethnic Cohort Study. Breast effect on survival in hormone receptor negative patients. Breast Cancer Res Treat Cancer Res Treat 2011; 129: 565–574. 1996; 41: 123–130. 90. Katoh A, Watzlaf VJ, D’Amico F. An examination of obesity and breast cancer 66. Shu XO, Zheng Y, Cai H et al. Soy food intake and breast cancer survival. JAMA survival in post-menopausal women. Br J Cancer 1994; 70: 928–933. 2009; 302: 2437–2443. 91. Rosenberg L, Czene K, Hall P. Obesity and poor breast cancer prognosis: an 67. Sparano JA, Wang M, Zhao F et al. Race and hormone receptor-positive breast illusion because of hormone replacement therapy? Br J Cancer 2009; 100: cancer outcomes in a randomized chemotherapy trial. J Natl Cancer Inst 2012; 1486–1491. 104: 406–414. 92. Schairer C, Gail M, Byrne C et al. Estrogen replacement therapy and breast 68. Vatten LJ, Foss OP, Kvinnsland S. Overall survival of breast cancer patients in cancer survival in a large screening study. J Natl Cancer Inst 1999; 91: relation to preclinically determined total serum cholesterol, body mass index, 264–270. height and cigarette smoking: a population-based study. Eur J Cancer 1991; 27: 93. Zhang S, Folsom AR, Sellers TA et al. Better breast cancer survival for 641–646. postmenopausal women who are less overweight and eat less fat. The Iowa 69. Allemani C, Berrino F, Krogh V et al. Do pre-diagnostic drinking habits influence Women’s Health Study. Cancer 1995; 76: 275–283. breast cancer survival? Tumori 2011; 97: 142–148. 94. Pfeiler G, Stoger H, Dubsky P et al. Efficacy of tamoxifen+/-aminoglutethimide in 70. Gregorio DI, Emrich LJ, Graham S et al. Dietary fat consumption and survival normal weight and overweight postmenopausal patients with hormone receptor- among women with breast cancer. J Natl Cancer Inst 1985; 75: 37–41. positive breast cancer: an analysis of 1509 patients of the ABCSG-06 trial. Br J Cancer 2013; 108: 1408–1414. 71. Kyogoku S, Hirohata T, Takeshita S et al. Survival of breast-cancer patients and body size indicators. Int J Cancer 1990; 46: 824–831. 95. Loi S, Milne RL, Friedlander ML et al. Obesity and outcomes in premenopausal and postmenopausal breast cancer. Cancer Epidemiol Biomarkers Prev 2005; 72. Mohle-Boetani JC, Grosser S, Whittemore AS et al. Body size, reproductive 14: 1686–1691. factors, and breast cancer survival. Prev Med 1988; 17: 634–642. 96. Mason BH, Holdaway IM, Stewart AW et al. Season of tumour detection 73. Suissa S, Pollak M, Spitzer WO et al. Body size and breast cancer prognosis: a influences factors predicting survival of patients with breast cancer. Breast statistical explanation of the discrepancies. Cancer Res 1989; 49: 3113–3116. Cancer Res Treat 1990; 15: 27–37. 74. Allin KH, Nordestgaard BG, Flyger H et al. Elevated pre-treatment levels of 97. Moon HG, Han W, Noh DY. Underweight and breast cancer recurrence and death: plasma C-reactive protein are associated with poor prognosis after breast cancer: a report from the Korean Breast Cancer Society. J Clin Oncol 2009; 27: a cohort study. Breast Cancer Res 2011; 13: R55. 5899–5905. 75. den Tonkelaar I, de WF, Seidell JC et al. Obesity and subcutaneous fat patterning 98. Lee K-H, Keam B, Im S-A et al. Body mass index is not associated with treatment in relation to survival of postmenopausal breast cancer patients participating in outcomes of breast cancer patients receiving neoadjuvant chemotherapy: Korean the DOM-project. Breast Cancer Res Treat 1995; 34: 129–137. data. J Breast Cancer 2012; 15: 427–433. 76. Eley JW, Hill HA, Chen VW et al. Racial differences in survival from breast cancer. 99. Chen X, Lu W, Zheng W et al. Obesity and weight change in relation to breast Results of the National Cancer Institute Black/White Cancer Survival Study. JAMA cancer survival. Breast Cancer Res Treat 2010; 122: 823–833. 1994; 272: 947–954. 100. Tao MH, Shu XO, Ruan ZX et al. Association of overweight with breast cancer 77. Gordon NH, Crowe JP, Brumberg DJ et al. Socioeconomic factors and race in survival. Am J Epidemiol 2006; 163: 101–107. breast cancer recurrence and survival. Am J Epidemiol 1992; 135: 609–618. Volume 25 | No. 10 | October 2014 doi:10.1093/annonc/mdu042 |  Annals of Oncology reviews 101. Hou G, Zhang S, Zhang X et al. Clinical pathological characteristics and 111. Ademuyiwa FO, Groman A, O’Connor T et al. Impact of body mass index on prognostic analysis of 1,013 breast cancer patients with diabetes. Breast Cancer clinical outcomes in triple-negative breast cancer. Cancer 2011; 117: Res Treat 2013; 137: 807–816. 4132–4140. 102. Kawai M, Minami Y, Nishino Y et al. Body mass index and survival after breast 112. Nichols HB, Trentham-Dietz A, Egan KM et al. Body mass index before and after cancer diagnosis in Japanese women. BMC Cancer 2012; 12: 149. breast cancer diagnosis: associations with all-cause, breast cancer, and cardiovascular disease mortality. Cancer Epidemiol Biomarkers Prev 2009; 18: 103. Labidi SI, Mrad K, Mezlini A et al. Inflammatory breast cancer in Tunisia in the era 1403–1409. of multimodality therapy. Ann Oncol 2008; 19: 473–480. 113. Dignam JJ, Wieand K, Johnson KA et al. Effects of obesity and race on prognosis 104. Berclaz G, Li S, Price KN et al. Body mass index as a prognostic feature in in lymph node-negative, estrogen receptor-negative breast cancer. Breast Cancer operable breast cancer: the International Breast Cancer Study Group experience. Res Treat 2006; 97: 245–254. Ann Oncol 2004; 15: 875–884. 114. Dignam JJ, Wieand K, Johnson KA et al. Obesity, tamoxifen use, and outcomes 105. Ewertz M, Gray KP, Regan MM et al. Obesity and risk of recurrence or death after in women with estrogen receptor-positive early-stage breast cancer. J Natl adjuvant endocrine therapy with letrozole or tamoxifen in the breast international Cancer Inst 2003; 95: 1467–1476. group 1–98 trial. J Clin Oncol 2012; 30: 3967–3975. 115. Majed B, Moreau T, Senouci K et al. Is obesity an independent prognosis factor in 106. Keegan TH, Milne RL, Andrulis IL et al. Past recreational physical activity, body woman breast cancer? Breast Cancer Res Treat 2008; 111: 329–342. size, and all-cause mortality following breast cancer diagnosis: results from the Breast Cancer Family Registry. Breast Cancer Res Treat 2010; 123: 531–542. 116. Dawood S, Broglio K, Gonzalez-Angulo AM et al. Prognostic value of body mass index in locally advanced breast cancer. Clin Cancer Res 2008; 14: 1718–1725. 107. von Drygalski A, Tran TB, Messer K et al. Obesity is an independent predictor of poor survival in metastatic breast cancer: retrospective analysis of a patient 117. Flatt SW, Thomson CA, Gold EB et al. Low to moderate alcohol intake is not cohort whose treatment included high-dose chemotherapy and autologous stem associated with increased mortality after breast cancer. Cancer Epidemiol cell support. Int J Breast Cancer 2011; 523276. doi:10.4061/2011/523276 Biomarkers Prev 2010; 19: 681–688. 108. Ewertz M, Jensen MB, Gunnarsdottir KA et al. Effect of obesity on prognosis after 118. Kwan ML, Chen WY, Kroenke CH et al. Pre-diagnosis body mass index and early-stage breast cancer. J Clin Oncol 2011; 29: 25–31. survival after breast cancer in the After Breast Cancer Pooling Project. Breast Cancer Res Treat 2012; 132: 729–739. 109. Tornberg S, Carstensen J. Serum beta-lipoprotein, serum cholesterol and Quetelet’s index as predictors for survival of breast cancer patients. Eur J Cancer 119. Rock CL, Doyle C, Demark-Wahnefried W et al. Nutrition and physical activity 1993; 29A: 2025–2030. guidelines for cancer survivors. CA Cancer J Clin 2012; 62: 243–274. 110. Newman SC, Lees AW, Jenkins HJ. The effect of body mass index and oestrogen 120. Griggs JJ, Mangu PB, Anderson H et al. Appropriate chemotherapy dosing for receptor level on survival of breast cancer patients. Int J Epidemiol 1997; 26: obese adult patients with cancer: American Society of Clinical Oncology clinical 484–490. practice guideline. J Clin Oncol 2012; 30: 1553–1561. Annals of Oncology 25: 1914–1918, 2014 doi:10.1093/annonc/mdu052 Published online 25 February 2014 Prediction of treatment-related toxicity and outcome with geriatric assessment in elderly patients with solid malignancies treated with chemotherapy: a systematic review 1 1 2 3 1 K. S. Versteeg , I. R. Konings , A. M. Lagaay , A. A. van de Loosdrecht & H. M. W. Verheul 1 2 3 Department of Medical Oncology, VU University Medical Center, Amsterdam; Department of Internal Medicine, Spaarne Hospital, Hoofddorp; Department of Hematology, VU University Medical Center, Amsterdam, The Netherlands Received 10 July 2013; revised 9 December 2013 & 3 February 2014; accepted 4 February 2014 Introduction: The number of older patients with cancer is increasing. Standard clinical evaluation of these patients may not be sufficient to determine individual treatment strategies and therefore Geriatric Assessment (GA) may be of clinical value. In this review, we summarize current literature that is available on GA in elderly patients with solid malignancies who receive chemotherapy. We focus on prediction of treatment toxicity, mortality and the role of GA in the decision-making process. *Correspondence to: Professor H. M. W. Verheul, Department of Medical Oncology, VU University Medical Center, Amsterdam, Netherlands. Kamer ZH 3A44, De Boelelaan 1117, 1081 HVAmsterdam, The Netherlands. Tel: +31-20-4444300; Fax: +31-20-4444079; E-mail: h.verheul@vumc.nl © The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Oncology Pubmed Central

Body mass index and survival in women with breast cancer—systematic literature review and meta-analysis of 82 follow-up studies

Loading next page...
 
/lp/pubmed-central/body-mass-index-and-survival-in-women-with-breast-cancer-systematic-Ue3Ocn0iOK

References (130)

Publisher
Pubmed Central
Copyright
© The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology.
ISSN
0923-7534
eISSN
1569-8041
DOI
10.1093/annonc/mdu042
Publisher site
See Article on Publisher Site

Abstract

Annals of Oncology reviews Annals of Oncology 25: 1901–1914, 2014 doi:10.1093/annonc/mdu042 Published online 27 April 2014 Body mass index and survival in women with breast cancer—systematic literature review and meta-analysis of 82 follow-up studies 1 1 1,2 3 4 5 D. S. M. Chan , A. R. Vieira , D. Aune , E. V. Bandera , D. C. Greenwood , A. McTiernan , 1 6,7 8 1 D. Navarro Rosenblatt , I. Thune , R. Vieira & T. Norat 1 2 Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; Department of Public Health and General Practice, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway; Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New 4 5 Jersey, New Jersey, USA; Division of Biostatistics, Centre for Epidemiology and Biostatistics, University of Leeds, Leeds, UK; Division of Public Health Sciences, Fred 6 7 Hutchinson Cancer Research Center, Washington, USA; Department of Oncology, Oslo University Hospital, Oslo; Faculty of Health Sciences, Department of Community Medicine, University of Tromso, Tromso, Norway; School of Mathematics and Statistics, University of Newcastle, Newcastle upon Tyne, UK Received 12 December 2013; accepted 16 January 2014 Background: Positive association between obesity and survival after breast cancer was demonstrated in previous meta-analyses of published data, but only the results for the comparison of obese versus non-obese was summarised. Methods: We systematically searched in MEDLINE and EMBASE for follow-up studies of breast cancer survivors with body mass index (BMI) before and after diagnosis, and total and cause-specific mortality until June 2013, as part of the World Cancer Research Fund Continuous Update Project. Random-effects meta-analyses were conducted to explore the magnitude and the shape of the associations. Results: Eighty-two studies, including 213 075 breast cancer survivors with 41 477 deaths (23 182 from breast cancer) were identified. For BMI before diagnosis, compared with normal weight women, the summary relative risks (RRs) of total mortality were 1.41 [95% confidence interval (CI) 1.29–1.53] for obese (BMI >30.0), 1.07 (95 CI 1.02–1.12) for overweight (BMI 25.0–<30.0) and 1.10 (95% CI 0.92–1.31) for underweight (BMI <18.5) women. For obese women, the summary RRs were 1.75 (95% CI 1.26–2.41) for pre-menopausal and 1.34 (95% CI 1.18–1.53) for post-menopausal breast cancer. For each 5 kg/m increment of BMI before, <12 months after, and ≥12 months after diagnosis, increased risks of 17%, 11%, and 8% for total mortality, and 18%, 14%, and 29% for breast cancer mortality were observed, respectively. Conclusions: Obesity is associated with poorer overall and breast cancer survival in pre- and post-menopausal breast cancer, regardless of when BMI is ascertained. Being overweight is also related to a higher risk of mortality. Randomised clinical trials are needed to test interventions for weight loss and maintenance on survival in women with breast cancer. Key words: body mass index, meta-analysis, survival after breast cancer, systematic literature review obesity [4], which has further been linked to breast cancer recur- introduction rence [5] and poorer survival in pre- and post-menopausal The number of female breast cancer survivors is growing because breast cancer [6, 7]. Preliminary findings from randomised, con- of longer survival as a consequence of advances in treatment and trolled trials suggest that lifestyle modifications improved bio- early diagnosis. There were ∼2.6 million female breast cancer sur- markers associated with breast cancer progression and overall vivors in US in 2008 [1], and in the UK, breast cancer accounted survival [8]. for ∼28% of the 2 million cancer survivors in 2008 [2]. The biological mechanisms underlying the association between Obesity is a pandemic health concern, with over 500 million obesity and breast cancer survival are not established, and could adults worldwide estimated to be obese and 958 million were involve interacting mediators of hormones, adipocytokines, and overweight in 2008 [3]. One of the established risk factors for inflammatory cytokines which link to cell survival or apoptosis, breast cancer development in post-menopausal women is migration, and proliferation [9]. Higher level of oestradiol pro- duced in postmenopausal women through aromatisation of androgens in the adipose tissues [10], and higher level of insulin *Correspondence to: Doris S. M. Chan, Department of Epidemiology and Biostatistics, [11], a condition common in obese women, are linked to poorer School of Public Health, Imperial College London, St Mary’s Campus, Norfolk Place, prognosis in breast cancer. A possible interaction between leptin London W2 1PG, UK. Tel: +44-0-20-759-48590; Fax: +44-0-20-759-43193; and insulin [12], and obesity-related markers of inflammation E-mail: d.chan@imperial.ac.uk © The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Annals of Oncology reviews [13] have also been linked to breast cancer outcomes. Non-bio- statistical analysis logical mechanisms could include chemotherapy under-dosing in Categorical and dose–response meta-analyses were conducted using random-effects models to account for between-study het- obese women, suboptimal treatment, and obesity-related compli- cations [14]. erogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each Numerous studies have examined the relationship between obesity and breast cancer outcomes, and past reviews have con- study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component cluded that obesity is linked to a lower survival; however, when investigated in a meta-analysis of published data, only the results which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used of obese compared with non-obese or lighter women were summarised [6, 7, 15]. for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also We carried out a systematic literature review and meta-ana- lysis of published studies to explore the magnitude and the included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet’s Index (QI) measured in shape of the association between body fatness, as measured by body mass index (BMI), and the risk of total and cause-specific units of kg/m was used. We conducted categorical meta-analyses by pooling the cat- mortality, overall and in women with pre- and post-menopausal breast cancer. As body weight may change close to diagnosis egorical results reported in the studies. The studies used differ- ent BMI categories. In some studies, underweight (BMI <18.5 and during primary treatment of breast cancer [16], we exam- ined BMI in three periods: before diagnosis, <12 months after kg/m according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m ) were classified diagnosis, and ≥12 months after breast cancer diagnosis. together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 2 2 materials and methods kg/m ) and obese (BMI ≥30.0 kg/m ) women separately but, in some studies, overweight and obese women were combined. The data sources and search reference category was normal weight or underweight together We carried out a systematic literature search, limited to publica- with normal weight, depending on the studies. For convenience, tions in English, for articles on BMI and survival in women with the BMI categories are referred to as underweight, normal breast cancer in OVID MEDLINE and EMBASE from inception weight, overweight, and obese in the present review. We derived to 30 June 2013 using the search strategy implemented for the the RRs for overweight and obese women compared with WCRF/AICR Continuous Update Project on breast cancer sur- normal weight women in two studies [19, 21] that had more vival. The search strategy contained medical subject headings and than four BMI categories using the method of Hamling et al. text words that covered a broad range of factors on diet, physical [22]. Studies that reported results for obese compared with non- activity, and anthropometry. The protocol for the review is obese women were analysed separately. available at http://www.dietandcancerreport.org/index.php [17]. The non-linear dose–response relationship between BMI and In addition, we hand-searched the reference lists of relevant arti- mortality was examined using the best-fitting second-order frac- cles, reviews, and meta-analysis papers. tional polynomial regression model [23], defined as the one with the lowest deviance. Non- linearity was tested using the likelihood ratio test [24]. In the study selection non-linear meta-analysis, the reference category was the lowest Included were follow-up studies of breast cancer survivors, BMI category in each study and RRs were recalculated using the which reported estimates of the associations of BMI ascertained method of Hamling et al. [22] when the reference category was before and after breast cancer diagnosis with total or cause- not the lowest BMI category in the study. specific mortality risks. Studies that investigated BMI after diag- We also conducted linear dose–response meta-analyses, ex- nosis were divided into two groups: BMI <12 months after diag- cluding the category underweight when reported separately in nosis (BMI <12 months) and BMI 12 months or more after the studies, by pooling estimates of RR per unit increase (with diagnosis (BMI ≥12 months). Outcomes included total mortal- its standard error) provided by the studies, or derived by us ity, breast cancer mortality, death from cardiovascular disease, from categorical data using generalised least-squares for trend and death from causes other than breast cancer. When multiple estimation [25]. To estimate the trend, the numbers of publications on the same study population were found, results outcomes and population at-risk for at least three BMI categor- based on longer follow-up and more outcomes were selected for ies, or the information required to derive them using standard the meta-analysis. methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the cat- data extraction egories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended DSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related infor- category to estimate the midpoints. Where the RRs were pre- sented by subgroups (age group [27], menopausal status [28, mation, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and popula- 29], stage [30] or subtype [31] of breast cancer, or others [32– 34]), an overall estimate for the study was obtained by a fixed- tion at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m of BMI. and adjustment factors in the analysis.  | Chan et al. Volume 25 | No. 10 | October 2014 Annals of Oncology reviews To assess heterogeneity, we computed the Cochran Q test and total, breast cancer or non-breast cancer mortality in obese I statistic [35]. The cut points of 30% and 50% were used for women (before or <12 months after diagnosis) compared with low, moderate, and substantial level of heterogeneity. Sources of the reference BMI [69, 71–74, 76, 77, 79, 82], two publications heterogeneity were explored by meta-regression and subgroup reported non-significant inverse associations [75, 80] and three analyses using pre-defined factors, including indicators of study publications reported no association [70, 78, 81] of BMI with quality (menopausal status, hormone receptor status, number of survival after breast cancer. Hence, 79 publications from 82 outcomes, length of follow-up, study design, geographic loca- follow-up studies with 41 477 deaths (23 182 from breast tion, BMI assessment, adjustment for confounders, and others). cancer) in 213 075 breast cancer survivors were included in the Small study or publication bias was examined by Egger’s test meta-analyses (Figure 1). Supplementary Table S1, available at [36] and visual inspection of the funnel plots. The influence of Annals of Oncology online shows the characteristics of the each individual study on the summary RR was examined by ex- studies included in the meta-analyses and details of the excluded cluding the study in turn [37]. A P value of <0.05 was consid- studies are in supplementary Table S2, available at Annals of ered statistically significant. All analyses were conducted using Oncology online. Results of the meta-analyses are summarised Stata version 12.1 (Stata Statistical Software: Release 12, in Table 1. StataCorp LP, College Station, TX). Studies were follow-up of women with breast cancer iden- tified in prospective aetiologic cohort studies (women were free of cancer at enrolment), or cohorts of breast cancer survivors results whose participants were identified in hospitals or through A total of 124 publications investigating the relationship of body cancer registries, or follow-up of breast cancer patients enrolled in case–control studies or randomised clinical trials. fatness and mortality in women with breast cancer were iden- tified. We excluded 31 publications, including four publications Some studies included only premenopausal women [83–85] or postmenopausal women [21, 27, 86–94], but most studies on other obesity indices [38–41], 12 publications without a measure of association [42–53], and 15 publications superseded included both. Menopausal status was usually determined at time of diagnosis. Year of diagnosis was from 1957–1965 [70]to by publications of the same study with more outcomes [54–68]. A further 14 publications were excluded because of insufficient 2002–2009 [74]. Patient tumour characteristics and stage of disease at diagnosis varied across studies, and some studies data for the meta-analysis (five publications [69–73]) or un- adjusted results (nine publications [74–82]), from which nine included carcinoma in situ. No all studies provided clinical in- formation on the tumour, treatment, and co-morbidities. publications reported statistically significant increased risk of 21 566 records excluded on the basis of title 22 590 unique records identified in MEDLINE and EMBASE until 30 June 2013 and abstract 19 articles found in handsearch 651 articles excluded for not fulfilling the inclusion criteria 87 no original data 338 did not report on the associations of 1043 full-text articles retrieved and assessed interest for inclusion 33 abstract/commentary 10 meta-analyses 183 irrelevant study design 268 articles did not investigate body 392 potentially relevant articles in women fatness and mortality with breast cancer 31 articles excluded in present review 4 examined obesity index 12 no measure of association 124 articles on body fatness and mortality 15 superseded publications 14 articles excluded in meta-analysis 5 insufficient data 8 unadjusted results 79 relevant articles (82 studies) on body mass index and mortality included in the meta-analyses in present review Figure 1. Flowchart of search. Volume 25 | No. 10 | October 2014 doi:10.1093/annonc/mdu042 |  Annals of Oncology reviews  | Chan et al. Volume 25 | No. 10 | October 2014 Table 1. Summary of meta-analyses of BMI and survival in women with breast cancer BMI before diagnosis BMI <12 months after diagnosis BMI ≥12 months after diagnosis 2 2 2 N RR (95% CI) I (%) N RR (95% CI) I (%) N RR (95% CI) I (%) P P P h h h Total mortality Under versus normal weight 10 1.10 (0.92–1.31) 48% 11 1.25 (0.99–0.57) 63% 3 1.29 (1.02–1.63) 0% 0.04 <0.01 0.39 Over versus normal weight 19 1.07 (1.02–1.12) 0% 22 1.07 (1.02–1.12) 21% 4 0.98 (0.86–1.11) 0% 0.88 0.18 0.72 Obese versus normal weight 21 1.41 (1.29–1.53) 38% 24 1.23 (1.12–1.33) 69% 5 1.21 (1.06–1.38) 0% 0.04 <0.01 0.70 Obese versus non-obese –– – 12 1.26 (1.07–1.47) 80% –– – <0.01 Per 5 kg/m increase 15 1.17 (1.13–1.21) 7% 12 1.11 (1.06–1.16) 55% 4 1.08 (1.01–1.15) 0% 0.38 0.01 0.52 Breast cancer mortality Under versus normal weight 8 1.02 (0.85–1.21) 31% 5 1.53 (1.27–1.83) 0% 1 1.10 (0.15–8.08) – 0.18 0.59 Over versus normal weight 21 1.11 (1.06–1.17) 0% 12 1.11 (1.03–1.20) 14% 2 1.37 (0.96–1.95) 0% 0.66 0.31 0.90 Obese versus normal weight 22 1.35 (1.24–1.47) 36% 12 1.25 (1.10–1.42) 53% 2 1.68 (0.90–3.15) 67% 0.05 0.02 0.08 Obese versus non-obese –– – 6 1.26 (1.05–1.51) 64% –– – 0.02 Per 5 kg/m increase 18 1.18 (1.12–1.25) 47% 8 1.14 (1.05–1.24) 66% 2 1.29 (0.97–1.72) 64% 0.01 0.01 0.10 Cardiovascular disease related mortality Over versus normal weight 2 1.01 (0.80–1.29) 0% –– – – – – 0.87 Obese versus normal weight 2 1.60 (0.66–3.87) 78% –– – – – – 0.03 Per 5 kg/m increase 2 1.21 (0.83–1.77) 80% –– – – – – 0.03 Non-breast cancer mortality Over versus normal weight –– – 5 0.96 (0.83–1.11) 26% –– – 0.25 Obese versus normal weight –– – 5 1.29 (0.99–1.68) 72% –– – 0.01 BMI before and after diagnosis (<12 months after, or ≥12 months after diagnosis) was classified according to the exposure period which the studies referred to in the BMI assessment; the BMI categories were included in the categorical meta-analyses as defined by the studies. P , P for heterogeneity between studies. h Annals of Oncology reviews % BMI Weight kg/m Study RR (95% Cl) Underweight v normal weight Buck 2011 1.98 (0.79, 4.96) 3.29 <18.5 v 18.5–24.9 Conroy 2011 1.13 (0.89, 1.42) 17.23 <22.5 v 22.5–24.9 Lu 2011 0.89 (0.70, 1.14) 16.79 <20 v 20–24.9 Chen 2010 1.44 (0.88, 2.37) 8.51 <=18.4 v 18.5–24.9 Emaus 2010 1.70 (0.86, 3.33) 5.44 <=18.4 v 18.5–24.9 < Hellmann 2010 1.36 (0.87, 2.11) 9.79 <=19.9 v 20–25 Nichols 2009 1.75 (0.94, 3.25) 6.21 <=18.4 v 18.5–24.9 Abrahamson 2006 0.73 (0.52, 1.04) 12.75 <=18.4 v 18.5–24.9 Kroenke 2005 0.89 (0.70, 1.13) 16.98 <21 v 21–22 Bernstein 2002 1.13 (0.43, 2.97) 3.01 <=18.4 v 18.5–24.9 Subtotal (I-squared = 48.2%, P = 0.043) 1.10 (0.92, 1.31) 100.00 Overweight v normal weight Kamineni 2013 1.09 (0.69, 1.72) 1.18 25–29.9 v <25 Buck 2011 1.03 (0.69, 1.56) 1.47 25–29.9 v 18.5–24.9 Conroy 2011 1.15 (0.93, 1.42) 5.47 25–29.9 v 22.5–24.9 Lu 2011 0.99 (0.84, 1.15) 9.94 25–29.9 v 20–24.9 Chen 2010 1.02 (0.81, 1.27) 4.85 25–29.9 v 18.5–24.9 Emaus 2010 1.02 (0.81, 1.27) 4.85 25–29.9 v 18.5–24.9 Hellmann 2010 1.22 (0.92, 1.61) 3.13 25.1–30 v 20–25 Keegan 2010 1.16 (0.92, 1.45) 4.74 25–29.9 v <=24.9 Nichols 2009 1.13 (0.90, 1.42) 4.71 25–29.9 v 18.5–24.9 West-Wright 2009 0.98 (0.78, 1.24) 4.56 25–29 v <=24 Caan 2008 1.20 (0.80, 1.70) 1.73 25–29.9 v <=24.9 Dal Maso 2008 1.02 (0.83, 1.25) 5.85 25–29.9 v <=24.9 Reding 2008 1.20 (0.90, 1.60) 2.96 >22.4–25.8 v <–20.6 Reeves 2007 1.02 (0.94, 1.12) 32.20 25–<30 v 18.5–<25 Abrahamson 2006 1.47 (0.96, 2.24) 1.37 25–29.9 v 18.5–24.9 Kroenke 2005 1.11 (0.91, 1.34) 6.55 25–29 v 21–22 Reeves 2000 1.19 (0.92, 1.53) 3.79 25–26 v <=24 Zhang 1995 1.00 (0.50, 2.20) 0.45 24.7–28.8 v 16–24.6 Holmberg 1994 2.38 (0.84, 6.77) 0.23 25–28 v <19 Subtotal (I-squared = 0.0%, P = 0.882) 1.07 (1.02, 1.12) 100.00 Obese v normal weight Kamineni 2013 1.31 (0.77, 2.22) 2.20 >=30 v <25 Buck 2011 1.15 (0.54, 2.46) 1.17 >=30 v 18.5–24.9 Conroy 2011 1.54 (1.23, 1.91) 7.31 >=30 v 22.5–24.9 Lu 2011 1.23 (1.04, 1.47) 8.97 >=30 v 20–24.9 Chen 2010 1.58 (1.13, 2.22) 4.42 >=30 v 18.5–24.9 Emaus 2010 1.47 (1.08, 1.99) 5.05 >=30 v 18.5–24.9 Hellmann 2010 1.61 (1.12, 2.33) 3.94 >30 v 20–25 Keegan 2010 1.21 (1.00, 1.48) 8.12 >=30 v <=24.9 Nichols 2009 1.52 (1.17, 1.98) 6.06 >=30 v 18.5–24.9 West-Wright 2009 1.42 (1.08, 1.88) 5.70 >=30 v <25 Caan 2008 1.60 (1.10, 2.30) 3.90 >=30 v <=24.9 Dal Maso 2008 1.29 (0.99, 1.68) 6.02 >=30 v <=24.9 Reding 2008 1.90 (1.40, 2.50) 5.39 >=25.8 v <=20.6 Cleveland 2007 1.63 (1.08, 2.45) 3.34 >30 v <24.9 Reeves 2007 1.06 (0.86, 1.30) 7.67 >=30 v 18.5–25 Abrahamson 2006 2.93 (1.37, 6.29) 1.16 >=30 v 18.5–24.9 Kroenke 2005 1.20 (0.95, 1.52) 6.85 >=30 v 21–22 Bernstein 2002 1.18 (0.81, 1.72) 3.78 >25 v 18.5–24.9 Reeves 2000 1.49 (1.18, 1.86) 7.08 >=27 v <=24 Zhang 1995 1.50 (0.70, 2.90) 1.31 28.9–45.9 v 16–24.6 Holmberg 1994 5.93 (1.98, 17.80) 0.58 >=29 v <19 Subtotal (I-squared = 37.6%, P = 0.043) 1.41 (1.29, 1.53) 100.00 0.125 1 8 Figure 2. Categorical meta-analysis of pre-diagnosis BMI and total mortality. Most of the studies were based in North America or Europe. 24 698 patients [97]. Total number of deaths ranged from 56 There were three studies from each of Australia [79, 95, 96], [93] to 7397 [108], and the proportion of deaths from breast Korea [97, 98] and China [99–101]; two studies from Japan [71, cancer ranged from 22% [27] to 98% [84] when reported. All 102]; one study from Tunisia [103] and four international but eight studies [30, 93, 94, 98, 99, 109–111] had an average studies [19, 104–106]. Study size ranged from 96 [107]to follow-up of more than 5 years. Volume 25 | No. 10 | October 2014 doi:10.1093/annonc/mdu042 |  Annals of Oncology reviews 2 2 BMI and total mortality women (I = 69%, P < 0.01; I = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 categorical meta-analysis. For BMI before diagnosis, compared (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI with normal weight women, the summary RRs were 1.41 (95% 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02– 1.02–1.63, 3 studies) for underweight women (supplementary 1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92– Figure S2, available at Annals of Oncology online). Twelve 1.31, 10 studies) for underweight women (Figure 2). For BMI additional studies reported results for obese versus non-obese <12 months after diagnosis and the same comparisons, the women <12 months after diagnosis, and the summary RR was summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for 1.26 (95% CI 1.07–1.47, I = 80%, P < 0.01). obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at dose–response meta-analysis. There was evidence of a J-shaped Annals of Oncology online). Substantial heterogeneities were association in the non-linear dose–response meta-analyses of observed between studies of obese women and underweight BMI before and after diagnosis with total mortality (all P < 0.01; Total mortality Breast cancer mortality Pre-diagnosis BMI Pre-diagnosis BMI 2 3 1.5 Best fitting fractional polynomial Best fitting fractional polynomial P < 0.001 P = 0.21 95% confidence interval 95% confidence interval 0.5 0.5 15 20 25 30 35 40 15 20 25 30 35 40 BMI (kg/m ) 2 BMI (kg/m ) BMI <12 months after diagnosis BMI <12 months after diagnosis Best fitting fractional polynomial Best fitting fractional polynomial 95% confidence interval 95% confidence interval P = 0.007 P = 1.00 0.5 0.5 15 20 25 30 35 40 15 20 25 30 35 40 BMI (kg/m ) BMI (kg/m ) BMI >12 months after diagnosis BMI >12 months after diagnosis Best fitting fractional polynomial Best fitting fractional polynomial 95% confidence interval 95% confidence interval 1.5 1 1.5 P < 0.001 P = 0.86 0.5 0.5 15 20 25 30 35 40 15 20 25 30 35 40 BMI (kg/m ) BMI (kg/m ) Figure 3. Non-linear dose–response curves of BMI and mortality.  | Chan et al. Volume 25 | No. 10 | October 2014 RR RR RR RR RR RR Annals of Oncology reviews Per 5 kg/m % Study BMI RR (95% Cl) Weight Pre-diagnosis BMI Kamineni 2013 1.14 (0.88, 1.47) 1.62 Conroy 2011 1.28 (1.14, 1.46) 6.95 Lu 2011 1.09 (1.00, 1.19) 13.36 Chen 2010 1.15 (1.01, 1.32) 5.79 Emaus 2010 1.14 (1.00, 1.30) 6.15 Hellmann 2010 1.26 (1.05, 1.52) 3.21 Nichols 2009 1.20 (1.06, 1.35) 7.26 West-Wright 2009 1.15 (1.01, 1.31) 5.89 Caan 2008 1.26 (1.05, 1.52) 3.12 Dal Maso 2008 1.11 (0.98, 1.26) 6.61 Reding 2008 1.17 (1.10, 1.23) 25.25 Abrahamson 2006 1.52 (1.16, 1.99) 1.49 Kroenke 2005 1.13 (1.02, 1.25) 9.06 Zhang 1995 1.14 (0.93, 1.39) 2.59 Helmberg 1994 1.47 (1.14, 1.89) 1.67 Subtotal (I-squared = 6.6%, P = 0.379) 1.17 (1.13, 1.21) 100.00 BMI <12 months after diagnosis Ewertz 2012 1.08 (0.99, 1.19) 10.73 Goodwin 2012 1.12 (0.94, 1.34) 4.97 Kawai 2012 1.52 (0.89, 2.60) 0.71 Baumgartner 2011 0.94 (0.83, 1.06) 7.91 Azambuja 2010 1.17 (1.06, 1.29) 10.00 Chen 2010 1.13 (0.99, 1.29) 7.31 Dawood 2008 1.12 (0.96, 1.30) 6.30 Majed 2008 1.05 (1.01, 1.10) 16.49 Vitolins 2008 1.22 (1.10, 1.34) 10.03 Abrahamson 2006 1.27 (1.11, 1.45) 7.20 Tao 2006 1.30 (1.01, 1.68) 2.78 Berclaz 2004 1.07 (1.02, 1.12) 15.58 Subtotal (I-squared = 54.8%, P = 0.011) 1.11 (1.06, 1.16) 100.00 BMI >=12 months after diagnosis Elatt 2010 1.11 (0.98, 1.27) 28.42 Nichols 2009 1.10 (0.98, 1.24) 35.22 Caan 2008 1.14 (0.92, 1.42) 10.34 Ewertz 1991 0.99 (0.86, 1.13) 26.01 Subtotal (I-squared = 0.0%, P = 0.517) 1.08 (1.01, 1.15) 100.00 0.5 1 2 Figure 4. Linear dose–response meta-analysis of BMI and total mortality. Figure 3), suggesting that underweight women may be at BMI and breast cancer mortality slightly increased risk compared with normal weight women. categorical meta-analysis. BMI was significantly associated The curves show linear increasing trends from 20 kg/m for with breast cancer mortality. Compared with normal weight BMI before diagnosis and <12 months after diagnosis, and from women, for BMI before diagnosis, the summary RRs were 1.35 25 kg/m for BMI ≥12 months after diagnosis. When linear (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI models were fitted excluding the underweight category, the 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI summary RRs of total mortality for each 5 kg/m increase in 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI after, and ≥12 months after diagnosis, respectively (Figure 4). 1.27–1.83, 5 studies) for underweight women (supplementary Substantial heterogeneity was observed between studies on BMI Figure S3, available at Annals of Oncology online). Substantial <12 months after diagnosis (I = 55%, P = 0.01). heterogeneity was observed between studies of obese women Volume 25 | No. 10 | October 2014 doi:10.1093/annonc/mdu042 |  Annals of Oncology reviews % BMI Weight kg/m Study RR (95% Cl) Underweight v normal weight Alsaker 2011 0.66 (0.38, 1.16) 8.17 <20 v 20–24.9 Conroy 2011 1.22 (0.87, 1.71) 16.83 <22.5 v 22.5–24.9 Lu 2011 0.86 (0.65, 1.12) 21.45 <20 v 20–24.9 Emaus 2010 1.49 (0.66, 3.37) 4.25 <18.5 v 18.5–25 Hellmann 2010 1.77 (0.99, 3.18) 7.60 <=19.9 v 20–25 Nichols 2009 0.93 (0.22, 3.85) 1.48 <=18.4 v 18.5–24.9 Kroenke 2005 0.88 (0.65, 1.19) 19.17 <21 v 21–22 Whiteman 2005 1.07 (0.81, 1.41) 21.05 <=18.5 v 18.51–22.90 Subtotal (I-squared = 31.1%, P = 0.179) 1.02 (0.85, 1.21) 100.00 Overweight v normal weight Kamineni 2013 1.45 (0.62, 3.39) 0.39 25–29.9 v <25 Alsaker 2011 1.14 (0.96, 1.35) 9.70 25–29.9 v 20–24.9 Conroy 2011 1.17 (0.86, 1.58) 3.05 25–29.9 v 22.5–24.9 Lu 2011 0.99 (0.83, 1.18) 9.10 25–29.9 v 20–24.9 Emaus 2010 1.01 (0.79, 1.30) 4.54 25.1–29.9 v 18.5–25 Hellmann 2010 1.23 (0.84, 1.79) 1.97 25.1–30 v 20–25 Nichols 2009 1.48 (0.98, 2.24) 1.65 25–29.9 v 18.5–24.9 Rosenberg 2009 0.90 (0.70, 1.20) 3.88 25–30 v <=24.9 West-Wright 2009 1.16 (0.83, 1.62) 2.52 25–29 v <=24 Caan 2008 1.40 (0.80, 2.30) 1.01 25–29.9 v <=24.9 Dal Maso 2008 1.07 (0.85, 1.35) 5.27 25–29.9 v <=24.9 Reeves 2007 1.05 (0.88, 1.26) 8.74 25–<30 v 18.5–<25 Kroenke 2005 1.12 (0.88, 1.43) 4.78 25–29 v 21–22 Whiteman 2005 1.25 (1.08, 1.44) 13.62 25–29.9 v <=22.9 Enger 2004 0.89 (0.63, 1.26) 2.35 22–24.8 v <20.4 Maehle 2004 1.03 (0.87, 1.40) 4.98 Q2-Q4 v Q1 Schairer 1999 1.30 (0.90, 1.70) 2.79 23.34–26.15 v <=21.28 Galanis 1998 1.70 (0.60, 4.50) 0.28 22.7–25.7 v <=22.6 Jain 1994 1.20 (0.75, 1.91) 1.29 24.14–27.34 v <=22.21 Tornberg 1993 1.40 (1.00, 1.90) 2.74 26–27 v <=21 Tretli 1990 1.09 (0.95, 1.24) 15.38 Q4 v Q1 Subtotal (I-squared = 0.0%, P = 0.658) 1.11 (1.06, 1.17) 100.00 Obese v normal weight Kamineni 2013 2.41 (1.00, 5.81) 0.88 >=30 v <25 Alsaker 2011 1.52 (1.25, 1.85) 8.27 >=30 v 20–24 Conroy 2011 1.45 (1.05, 2.00) 4.73 >=30 v 22.5–24.9 Lu 2011 1.20 (0.99, 1.46) 8.34 >=30 v 20–24.9 Emaus 2010 1.43 (1.01, 2.02) 4.28 >=30 v 18.5–25 Hellmann 2010 1.82 (1.11, 2.99) 2.46 >30 v 20–25 1.42 (0.86, 2.36) >=30 v 18.5–24.9 Nichols 2009 2.38 Rosenberg 2009 1.20 (0.90, 1.60) 5.49 >30 v <25 >=30 v <25 West-Wright 2009 1.71 (1.16, 2.53) 3.60 Caan 2008 1.60 (0.90, 2.70) 2.06 >=30 v <=24.9 >=30 v <=24.9 Dal Maso 2008 1.38 (1.02, 1.86) 5.19 Cleveland 2007 1.88 (1.04, 3.34) 1.86 >30 v <24.9 >=30 v 18.5–<25 Reeves 2007 1.12 (0.73, 1.73) 3.03 Kroenke 2005 1.09 (0.80, 1.48) 5.04 >=30 v 21–22 >=30 v <=22.9 Whiteman 2005 1.34 (1.09, 1.65) 7.86 Enger 2004 0.76 (0.53, 1.07) 4.20 >=24.9 v <20.4 Q5 v Q1 Maehle 2004 1.38 (1.04, 1.84) 5.55 Schairer 1999 1.60 (1.20, 2.10) 5.68 >=26.15 v <=21.28 >=25.8 v <=22.6 Galanis 1998 2.20 (0.90, 5.40) 0.85 Jain 1994 0.78 (0.48, 1.22) 2.71 >27.34 v <22.22 >=28 v <=21 Tornberg 1993 1.70 (1.20, 2.30) 4.67 Tretli 1990 1.35 (1.18, 1.54) 10.87 Q5 v Q1 Subtotal (I-squared = 35.5%, P = 0.051) 1.35 (1.24, 1.47) 100.00 0.125 1 8 Figure 5. Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality. (I = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the dose–response meta-analysis. There was no significant evidence summary RRs of the two studies identified were 1.68 (95% CI of a non-linear relationship between BMI before, <12 months 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for after, and ≥12 months after diagnosis and breast cancer overweight women (supplementary Figure S4, available at mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). Annals of Oncology online). The summary of another six studies When linear models were fitted excluding data from the that reported RRs for obese versus non-obese <12 months after underweight category, statistically significant increased risks of diagnosis was 1.26 (95% CI 1.05–1.51, I = 64%, P = 0.02). breast cancer mortality with BMI before and <12 months after  | Chan et al. Volume 25 | No. 10 | October 2014 Annals of Oncology reviews Per 5 kg/m % BMI RR (95% Cl) Weight Study Pre-diagnosis BMI Kamineni 2013 1.55 (1.00, 2.40) 1.43 Alsaker 2011 1.23 (1.12, 1.36) 9.57 Conroy 2011 1.24 (1.03, 1.48) 5.65 Lu 2011 1.08 (0.98, 1.19) 9.78 Hellmann 2010 1.33 (1.04, 1.70) 3.76 Nichols 2009 1.22 (0.97, 1.53) 4.20 Rosenberg 2009 1.07 (0.93, 1.23) 7.40 West-Wright 2009 1.28 (1.06, 1.55) 5.31 Caan 2008 1.27 (0.96, 1.67) 3.15 Dal Maso 2008 1.15 (1.00, 1.33) 7.23 Cleveland 2007 1.40 (1.09, 1.81) 3.50 Kroenke 2005 1.05 (0.92, 1.21) 7.54 Whiteman 2005 1.17 (1.07, 1.27) 10.33 Enger 2004 0.85 (0.66, 1.09) 3.64 Schairer 1999 1.36 (1.14, 1.62) 5.69 Galanis 1998 1.61 (1.02, 2.54) 1.34 Jain 1994 0.95 (0.78, 1.16) 4.86 Tornberg 1993 1.38 (1.15, 1.65) 5.64 Subtotal (I-squared = 47.3%, P = 0.014) 1.18 (1.12, 1.25) 100.00 BMI <12 months after diagnosis Hou 2013 0.98 (0.92, 1.05) 20.45 Kawai 2012 1.48 (0.80, 2.74) 1.76 Ranagopoulou 2012 1.13 (1.00, 1.27) 15.77 Sestak 2010 1.15 (1.05, 1.26) 18.09 Olsson 2009 1.11 (0.97, 1.26) 14.83 Vitolins 2008 1.22 (1.08, 1.36) 16.24 Hebert 1998 1.34 (1.01, 1.78) 6.43 Newman 1997 1.34 (1.01, 1.78) 6.44 Subtotal (I-squared = 65.8%, P = 0.005) 1.14 (1.05, 1.24) 100.00 BMI >=12 months after diagnosis Nichols 2009 1.48 (1.19, 1.83) 54.22 Caan 2008 1.10 (0.83, 1.44) 45.78 Subtotal (I-squared = 63.7%, P = 0.097) 1.29 (0.97, 1.72) 100.00 0.5 1 2 Figure 6. Linear dose–response meta-analysis of BMI and breast cancer mortality. diagnosis were observed (Figure 6). The summary RRs for each normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 2 2 5 kg/m increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 5 kg/m increase in BMI, the summary RR was 1.21 (95% CI breast cancer deaths) for BMI before diagnosis and 1.14 (95% 0.83–1.77). Five studies reported results for deaths from any cause CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. 2 2 months after diagnosis, with moderate (I = 47%, P = 0.01) and The summary RRs were 1.29 (95% CI 0.99–1.68, I =72%, 2 2 substantial (I = 66%, P = 0.01) heterogeneities between studies, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I =26%, respectively. Only two studies on BMI ≥12 months after P = 0.25) for overweight women compared with normal weight diagnosis and breast cancer mortality (N = 220 deaths) were women. identified. The summary RR was 1.29 (95% CI 0.97–1.72). BMI and other mortality outcomes subgroup, meta-regression, and sensitivity analyses Only two studies reported results for death from cardiovascular disease (N =151 deaths) [27, 112]. The summary RR for The results of the subgroup and meta-regression analyses are in obese versus normal weight before diagnosis was 1.60 (95% CI supplementary Tables S3 and S4, available at Annals of 0.66–3.87). No association was observed for overweight versus Oncology online. Subgroup analysis was not carried out for BMI Volume 25 | No. 10 | October 2014 doi:10.1093/annonc/mdu042 |  Annals of Oncology reviews ≥12 months after diagnosis as the limited number of studies associations observed in the linear dose–response meta-analysis. would hinder any meaningful comparisons. All associations were statistically significant, apart from the rela- Increased risks of mortality were observed in the meta-ana- tionship between BMI ≥12 months after diagnosis and breast lyses by menopausal status. While the summary risk estimates cancer mortality. This may be due to limited statistical power, seem stronger with premenopausal breast cancer, there was no with only 220 breast cancer deaths from two follow-up studies. significant heterogeneity between pre- and post-menopausal Positive associations, in some cases statistically significant, were breast cancer as shown in the meta-regression analyses also observed in overweight, and underweight women compared (P = 0.28–0.89) (supplementary Tables S3 and S4, available at with normal weight women. Women with BMI of 20 kg/m Annals of Oncology online). For BMI before diagnosis and total before, or <12 months after diagnosis, and of 25 kg/m mortality, the summary RRs for obese versus normal weight 12 months or more after diagnosis appeared to have the lowest were 1.75 (95% CI 1.26–2.41, I = 70%, P < 0.01, 7 studies) in mortality risk in the non-linear dose–response analysis. Co- women with pre-menopausal breast cancer and 1.34 (95% CI morbid conditions may cause the observed increased risk in 1.18–1.53, I = 27%, P = 0.20, 9 studies) in women with post- underweight women. Thorough investigation within the group menopausal breast cancer. and on their contribution to the shape of the association is hin- Studies with larger number of deaths [105, 115], conducted in dered, as not all studies in this review reported results for this Europe [28, 115], or with weight and height assessed through group. The increased risk associated with obesity was similar in medical records [28, 104, 115, 116] tended to report weaker pre- or post-menopausal breast cancer. We did not find any associations for BMI <12 months after diagnosis and total mor- evidence of a protective effect of obesity on survival after pre- tality compared with other studies (meta-regression P = 0.01, menopausal breast cancer, contrary to what has been 0.02, 0.01, respectively) (supplementary Table S3, available at observed for the development of breast cancer in pre-menopausal Annals of Oncology online); while studies with larger number of women [4]. deaths [101], conducted in Asia [101, 102], or adjusted for A large body of evidence with 41 477 deaths (23 182 from co-morbidity [101, 102] reported weaker associations for BMI breast cancer) in over 210 000 breast cancer survivors was sys- <12 months after diagnosis and breast cancer mortality (meta- tematically reviewed in the present study. We carried out cat- regression P = 0.01, 0.02, 0.01, respectively) (supplementary egorical, linear, and non-linear dose–response meta-analyses to Table S4, available at Annals of Oncology online). examine the magnitude and the shape of the associations for Analyses stratified by study designs, or restricted to studies total and cause-specific mortality in underweight, overweight, with invasive cases only, early-stage non-metastatic cases only, and obese women by time periods before and after diagnosis or mammography screening detected cases only, or controlled that is important in relation to the population-at-risk and breast for previous diseases did not produce results that were material- cancer survivors. Our findings agree with and further extend the ly different from those obtained in the overall analyses (results results from previous meta-analyses. A review published in 2010 not shown). Summary risk estimates remained statistically sign- reported statistically significant increased risks of 33% of both ificant when each study was omitted in turn, except for BMI total and breast cancer mortality for obesity versus non-obesity ≥12 months after diagnosis and total mortality. The summary around diagnosis [7]. These estimates are slightly higher than RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m increase when ours, which may be explained by the different search periods Flatt et al. [117] which contributed 315 deaths was omitted. and inclusion criteria for the articles (33 studies and 15 studies included in the analyses, respectively). Another review pub- lished in 2012 further reported consistent positive associations small studies or publication bias of total and breast cancer mortality with higher versus lower Asymmetry was only detected in the funnel plots of BMI <12 BMI around diagnosis [6]. No significant differences were months after diagnosis and total mortality, and breast cancer observed by menopausal status or hormone receptor status. The mortality, which suggests that small studies with an inverse After Breast Cancer Pooling Project of four prospective cohort association are missing (plots not shown). Egger’s tests studies found differential effects of levels of pre-diagnosis were borderline significant (P = 0.05) or statistically significant obesity on survival [118]. Compared with normal weight (P = 0.03), respectively. women, significant or borderline significant increased risks of 81% of total and 40% of breast cancer mortality were only observed for morbidly obese (≥40 kg/m ) women and not for discussion women in other obesity categories. We observed statistically The present systematic literature review and meta-analysis of significant increased risks also for overweight women, probably follow-up studies clearly supports that, in breast cancer survi- because of a larger number of studies. We were unable to investi- vors, higher BMI is consistently associated with lower overall gate the associations with severely and morbidly obese women and breast cancer survival, regardless of when BMI is ascer- because only two studies included in this review reported such tained. The limited number of studies on death from cardiovas- results [19, 113]. Overall, our findings are consistent with previ- cular disease is also consistent with a positive association. For ous meta-analyses in showing elevated total and breast cancer before, <12 months after, and 12 months or more after breast mortality associated with higher BMI and support the current cancer diagnosis, compared with normal weight women, obese guidelines for breast cancer survivors to stay as lean as possible women had 41%, 23%, and 21% higher risk for total mortality, within the normal range of body weight [4], for overweight and 35%, 25%, and 68% increased risk for breast cancer mortal- women to avoid weight gain during treatment and for obese ity, respectively. The findings were supported by the positive women to lose weight after treatment [119].  | Chan et al. Volume 25 | No. 10 | October 2014 Annals of Oncology reviews The present review is limited by the challenges and flaws co-morbidities, it is prudent to maintain a healthy body weight encountered by the individual epidemiological studies evaluating (BMI 18.5–<25.0 kg/m ) throughout life. the body fatness–mortality relationship in breast cancer survivors. Most studies did not adjust for co-morbidities and assess inten- acknowledgements tional weight loss. Women with more serious health issues, and especially smokers, may lose weight but are at an increased risk of TN is the principal investigator of the Continuous Update mortality, and this might cause an apparent increased risk in Project at Imperial College London. TN and DSMC wrote the underweight women. Body weight information through the protocol and implemented the study with the advice of an natural history of the disease and treatment information were expert committee convened by WCRF. RV developed and usually not complete or available. Increase of body weight post- managed the database for the Continuous Update Project. diagnosis is common in women with breast cancer, particularly DSMC, TN, and DA did the literature search and study selec- during chemotherapy [16]. Chemotherapy under-dosing is a tions. DSMC, ARV, and DNR did the data extraction. DSMC common problem in obese women and may contribute to their carried out the statistical analyses. DCG was statistical adviser increased mortality [120]. Although several studies with pre-diag- and contributed to the statistical analyses. DSMC wrote the first nosis BMI adjusted for underlying illnesses or excluded the first draft of the original manuscript. EB, AM, and IT are panel few years of follow-up, reverse causation may have affected the members of the Continuous Update Project and advised on the results in studies that assessed BMI in women with cancer and interpretation of the review. All authors revised the manuscript. other illnesses. However, in these studies, the associations were DSMC takes responsibility for the integrity of the data and the similar to other studies. Possible survival benefit (subjects with accuracy of the data analysis. better prognostic factors survive) may be present in the survival cohorts, in which the range of BMI could be narrower, and may cause an underestimation of the association. funding Follow-up studies with variable characteristics were pooled in This work was supported by the World Cancer Research Fund the meta-analysis. Women identified in clinical trials may have International (grant number: 2007/SP01) (http://www.wcrf-uk. had specific tumour subtypes, with fewer co-morbidities, and org/). The funder of this study had no role in the decisions were more likely to receive protocol treatments with high treat- about the design and conduct of the study; collection, manage- ment completion rates. Women who were recruited through ment, analysis, or interpretation of the data; or the preparation, mammography screening programmes may have had healthier review, or approval of the manuscript. The views expressed lifestyles or access to medical facilities, and more likely to be in this review are the opinions of the authors. They may not diagnosed with in situ or early-stage breast cancer. Cancer de- represent the views of the World Cancer Research Fund tection methods, tumour classifications and treatment regimens International/American Institute for Cancer Research and may change over time, and may vary within (if follow-up is long) and differ from those in future updates of the evidence related to between studies, and could not be simply examined by using the food, nutrition, physical activity, and cancer survival. diagnosis or treatment date. We cannot rule out the effect of un- measured or residual confounding in our analysis. Nevertheless, most results were adjusted for multiple confounding factors, in- disclosure cluding tumour stage or other-related variables and stratified analyses by several key factors showed similar summary risk DCG reports personal fees from World Cancer Research Fund/ American Institute for Cancer Research, during the conduct of estimates. Small study or publication bias was observed in the analyses of BMI the study; grants from Danone, and grants from Kelloggs, outside the submitted work. AM reports personal fees from <12 months after diagnosis. However, the overall evidence is supported by large, well-designed studies and is unlikely to be Metagenics/Metaproteomics, personal fees from Pfizer, outside the submitted work. All remaining authors have declared no changed. We did not conduct analyses by race/ethnicity and treatment types as only limited studies had published results. conflicts of interest. Future studies of body fatness and breast cancer outcomes should aim to account for co-morbidities, separate intended and references unintended changes of body weight, and collect complete treat- ment information during study follow-up. Randomised clinical 1. American Cancer Society. Breast Cancer Facts & Figures 2011–2012. Atlanta: trials are needed to test interventions for weight loss and main- American Cancer Society, Inc. 2012. tenance on survival in women with breast cancer. 2. Maddams J, Brewster D, Gavin A et al. Cancer prevalence in the United Kingdom: estimates for 2008. Br J Cancer 2009; 101: 541–547. In conclusion, the present systematic literature review and 3. Finucane MM, Stevens GA, Cowan MJ et al. National, regional, and global trends meta-analysis extends and confirms the associations of obesity in body-mass index since 1980: systematic analysis of health examination with an unfavourable overall and breast cancer survival in pre- surveys and epidemiological studies with 960 country-years and 9.1 million and post-menopausal breast cancer, regardless of when BMI is participants. Lancet 2011; 377: 557–567. ascertained. Increased risks of mortality in underweight and 4. World Cancer Research Fund/American Institute for Cancer Research. Food, overweight women were also observed. Given the comparable Nutrition, Physical Activity, and the Prevention of Cancer: a Global Perspective. elevated risks with obesity in the development (for post- Washington DC: AICR 2007. menopausal women) and prognosis of breast cancer, and the 5. Ligibel J. Obesity and breast cancer. Oncology (Williston Park) 2011; 25: complications with cancer treatment and other obesity-related 994–1000. Volume 25 | No. 10 | October 2014 doi:10.1093/annonc/mdu042 |  Annals of Oncology reviews 6. Niraula S, Ocana A, Ennis M et al. Body size and breast cancer prognosis in 30. Tretli S, Haldorsen T, Ottestad L. The effect of pre-morbid height and weight on relation to hormone receptor and menopausal status: a meta-analysis. Breast the survival of breast cancer patients. Br J Cancer 1990; 62: 299–303. Cancer Res Treat 2012; 134: 769–781. 31. Sparano JA, Wang M, Zhao F et al. Obesity at diagnosis is associated with 7. Protani M, Coory M, Martin JH. Effect of obesity on survival of women with breast inferior outcomes in hormone receptor-positive operable breast cancer. Cancer cancer: systematic review and meta-analysis. Breast Cancer Res Treat 2010; 2012; 118: 5937–5946. 123: 627–635. 32. Lu Y, Ma H, Malone KE et al. Obesity and survival among black women and white 8. Pekmezi DW, Demark-Wahnefried W. Updated evidence in support of diet and women 35 to 64 years of age at diagnosis with invasive breast cancer. J Clin exercise interventions in cancer survivors. Acta Oncol 2011; 50: 167–178. Oncol 2011; 29: 3358–3365. 9. Hursting SD, Berger NA. Energy balance, host-related factors, and cancer 33. Olsson A, Garne JP, Tengrup I et al. Body mass index and breast cancer survival progression. J Clin Oncol 2010; 28: 4058–4065. in relation to the introduction of mammographic screening. Eur J Surg Oncol 2009; 35: 1261–1267. 10. Lonning PE. Aromatase inhibition for breast cancer treatment. Acta Oncol 1996; 35(Suppl 5): 38–43. 34. Connor AE, Baumgartner RN, Pinkston C et al. Obesity and risk of breast cancer mortality in Hispanic and non-Hispanic white women: the New Mexico Women’s 11. Goodwin PJ, Ennis M, Bahl M et al. High insulin levels in newly diagnosed breast Health Study. J Women’s Health 2013; 22: 368–377. cancer patients reflect underlying insulin resistance and are associated with components of the insulin resistance syndrome. Breast Cancer Res Treat 2009; 35. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat 114: 517–525. Med 2002; 21: 1539–1558. 12. Goodwin PJ, Ennis M, Fantus IG et al. Is leptin a mediator of adverse prognostic 36. Sterne JA, Gavaghan D, Egger M. Publication and related bias in meta-analysis: effects of obesity in breast cancer? J Clin Oncol 2005; 23: 6037–6042. power of statistical tests and prevalence in the literature. J Clin Epidemiol 2000; 53: 1119–1129. 13. Pierce BL, Ballard-Barbash R, Bernstein L et al. Elevated biomarkers of inflammation are associated with reduced survival among breast cancer patients. 37. Tobias A. Assessing the influence of a single study in meta-analysis. Stata Tech J Clin Oncol 2009; 27: 3437–3444. Bull 1999; 47: 15–17. 14. Greenman CG, Jagielski CH, Griggs JJ. Breast cancer adjuvant chemotherapy 38. Abe R, Kumagai N, Kimura M et al. Biological characteristics of breast cancer in dosing in obese patients: dissemination of information from clinical trials to obesity. Tohoku J Exp Med 1976; 120: 351–359. clinical practice. Cancer 2008; 112: 2159–2165. 39. Bastarrachea J, Hortobagyi GN, Smith TL et al. Obesity as an adverse prognostic 15. Ryu SY, Kim CB, Nam CM et al. Is body mass index the prognostic factor in factor for patients receiving adjuvant chemotherapy for breast cancer. Ann Intern breast cancer? A meta-analysis. J Korean Med Sci 2001; 16: 610–614. Med 1994; 120: 18–25. 16. Demark-Wahnefried W, Campbell KL, Hayes SC. Weight management and its 40. Donegan WL, Jayich S, Koehler MR. The prognostic implications of obesity for role in breast cancer rehabilitation. Cancer 2012; 118: 2277–2287. the surgical cure of breast cancer. Breast 1978; 4: 14–17. 17. World Cancer Research Fund/American Institute for Cancer Research: 41. Nomura AM, Marchand LL, Kolonel LN et al. The effect of dietary fat on breast Continuous Update Project (CUP). 2013. cancer survival among Caucasian and japanese women in Hawaii. Breast Cancer Res Treat 1991; 18(Suppl. 1): S135–S141. 18. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986; 7: 177–188. 42. Albain KS, Green S, LeBlanc M et al. Proportional hazards and recursive partitioning and amalgamation analyses of the Southwest Oncology Group node- 19. de Azambuja E, Caskill-Stevens W, Francis P et al. The effect of body mass index positive adjuvant CMFVP breast cancer data base: a pilot study. Breast Cancer on overall and disease-free survival in node-positive breast cancer patients treated Res Treat 1992; 22: 273–284. with docetaxel and doxorubicin-containing adjuvant chemotherapy: the experience of the BIG 02–98 trial. Breast Cancer Res Treat 2010; 119: 145–153. 43. Bergmann A, Bourrus NS, de Carvalho CM et al. Arm symptoms and overall survival in Brazilian patients with advanced breast cancer. Asian Pac J Cancer 20. Vitolins MZ, Kimmick GG, Case LD. BMI influences prognosis following surgery Prev 2011; 12: 2939–2942. and adjuvant chemotherapy for lymph node positive breast cancer. Breast J 2008; 14: 357–365. 44. Coates RJ, Clark WS, Eley JW et al. Race, nutritional status, and survival from breast cancer. J Natl Cancer Inst 1990; 82: 1684–1692. 21. Sestak I, Distler W, Forbes JF et al. Effect of body mass index on recurrences in tamoxifen and anastrozole treated women: an exploratory analysis from the ATAC 45. Crujeiras AB, Cueva J, Vieito M et al. Association of breast cancer and obesity in trial. J Clin Oncol 2010; 28: 3411–3415. a homogeneous population from Spain. J Endocrinol Invest 2012; 35: 681–685. 22. Hamling J, Lee P, Weitkunat R et al. Facilitating meta-analyses by deriving 46. Kimura M. Obesity as prognostic factors in breast cancer. Diabetes Res Clin Pract relative effect and precision estimates for alternative comparisons from a set of 1990; 10: S247–S251. estimates presented by exposure level or disease category. Stat Med 2008; 27: 47. Lara-Medina F, Perez-Sanchez V, Saavedra-Perez D et al. Triple-negative breast 954–970. cancer in Hispanic patients: high prevalence, poor prognosis, and association 23. Royston P, Ambler G, Sauerbrei W. The use of fractional polynomials to model with menopausal status, body mass index, and parity. Cancer 2011; 117: continuous risk variables in epidemiology. Int J Epidemiol 1999; 28: 964–974. 3658–3669. 24. Bagnardi V, Zambon A, Quatto P et al. Flexible meta-regression functions for 48. Lethaby AE, Mason BH, Harvey VJ et al. Survival of women with node negative modeling aggregate dose-response data, with an application to alcohol and breast cancer in the Auckland region. N Z Med J 1996; 109: 330–333. mortality. Am J Epidemiol 2004; 159: 1077–1086. 49. Sendur MAN, Aksoy S, Zengin N et al. Efficacy of adjuvant aromatase inhibitor in 25. Orsini N, Bellocco R, Greenland S. Generalized least squares for trend estimation hormone receptor-positive postmenopausal breast cancer patients according to of summarized dose-response data. Stata J 2006; 6: 40–57. the body mass index. Br J Cancer 2012; 107: 1815–1819. 26. Bekkering GE, Harris RJ, Thomas S et al. How much of the data published in 50. Singh AK, Pandey A, Tewari M et al. Obesity augmented breast cancer risk: a observational studies of the association between diet and prostate or bladder potential risk factor for Indian women. J Surg Oncol 2011; 103: 217–222. cancer is usable for meta-analysis? Am J Epidemiol 2008; 167: 1017–1026. 51. Taylor SG, Knuiman MW, Sleeper LA et al. Six-year results of the Eastern 27. Reeves KW, Faulkner K, Modugno F et al. Body mass index and mortality among Cooperative Oncology Group trial of observation versus CMFP versus CMFPT in older breast cancer survivors in the Study of Osteoporotic Fractures. Cancer postmenopausal patients with node-positive breast cancer. J Clin Oncol 1989; 7: Epidemiol Biomarkers Prev 2007; 16: 1468–1473. 879–889. 28. Baumgartner AK, Hausler A, Seifert-Klauss V et al. Breast cancer after hormone 52. Loehberg CR, Almstedt K, Jud SM et al. Prognostic relevance of Ki-67 in the replacement therapy—does prognosis differ in perimenopausal and primary tumor for survival after a diagnosis of distant metastasis. Breast Cancer postmenopausal women? Breast 2011; 20: 448–454. Res Treat 2013; 138: 899–908. 29. Cleveland RJ, Eng SM, Abrahamson PE et al. Weight gain prior to diagnosis and 53. Mousa U, Onur H, Utkan G. Is obesity always a risk factor for all breast cancer survival from breast cancer. Cancer Epidemiol Biomarkers Prev 2007; 16: patients? c-erbB2 expression is significantly lower in obese patients with early 1803–1811. stage breast cancer. Clin Transl Oncol 2012; 14: 923–930.  | Chan et al. Volume 25 | No. 10 | October 2014 Annals of Oncology reviews 54. Anderson SJ, Wapnir I, Dignam JJ et al. Prognosis after ipsilateral breast tumor 78. Menon KV, Hodge A, Houghton J et al. Body mass index, height and cumulative recurrence and locoregional recurrences in patients treated by breast-conserving menstrual cycles at the time of diagnosis are not risk factors for poor outcome in therapy in five National Surgical Adjuvant Breast and Bowel Project protocols of breast cancer. Breast 1999; 8: 328–333. node-negative breast cancer. J Clin Oncol 2009; 27: 2466–2473. 79. Rohan TE, Hiller JE, McMichael AJ. Dietary factors and survival from breast 55. Bayraktar S, Hernadez-Aya LF, Lei X et al. Effect of metformin on survival cancer. Nutr Cancer 1993; 20: 167–177. outcomes in diabetic patients with triple receptor-negative breast cancer. Cancer 80. Saxe GA, Rock CL, Wicha MS et al. Diet and risk for breast cancer recurrence 2012; 118: 1202–1211. and survival. Breast Cancer Res Treat 1999; 53: 241–253. 56. Daling JR, Malone KE, Doody DR et al. Relation of body mass index to tumor 81. Schuetz F, Diel IJ, Pueschel M et al. Reduced incidence of distant markers and survival among young women with invasive ductal breast metastases and lower mortality in 1072 patients with breast cancer with a carcinoma. Cancer 2001; 92: 720–729. history of hormone replacement therapy. Am J Obstet Gynecol 2007; 196: 57. Eralp Y, Smith TL, Altundag K et al. Clinical features associated with a favorable 342–349. outcome following neoadjuvant chemotherapy in women with localized breast 82. Tammemagi CM, Nerenz D, Neslund-Dudas C et al. Comorbidity and survival cancer aged 35 years or younger. J Cancer Res Clin Oncol 2009; 135: disparities among black and white patients with breast cancer. JAMA 2005; 141–148. 294: 1765–1772. 58. Ewertz M. Breast cancer in Denmark. Incidence, risk factors, and characteristics 83. Enger SM, Bernstein L. Exercise activity, body size and premenopausal breast of survival. Acta Oncol 1993; 32: 595–615. cancer survival. Br J Cancer 2004; 90: 2138–2141. 59. Ganz PA, Habel LA, Weltzien EK et al. Examining the influence of beta blockers 84. Holmberg L, Lund E, Bergstrom R et al. Oral contraceptives and prognosis in and ACE inhibitors on the risk for breast cancer recurrence: results from the breast cancer: effects of duration, latency, recency, age at first use and relation LACE cohort. Breast Cancer Res Treat 2011; 129: 549–556. to parity and body mass index in young women with breast cancer. Eur J Cancer 60. Goodwin PJ, Ennis M, Pritchard KI et al. Fasting insulin and outcome in early- 1994; 30A: 351–354. stage breast cancer: results of a prospective cohort study. J Clin Oncol 2002; 85. Reding KW, Daling JR, Doody DR et al. Effect of prediagnostic alcohol 20: 42–51. consumption on survival after breast cancer in young women. Cancer Epidemiol 61. Greenberg ER, Vessey MP, McPherson K et al. Body size and survival in Biomarkers Prev 2008; 17: 1988–1996. premenopausal breast cancer. Br J Cancer 1985; 51: 691–697. 86. Alsaker MDK, Opdahl S, Asvold BO et al. The association of reproductive factors 62. Holmes MD, Stampfer MJ, Colditz GA et al. Dietary factors and the survival of and breastfeeding with long term survival from breast cancer. Breast Cancer Res women with breast carcinoma.[Erratum appears in Cancer 1999 Dec 15;86 Treat 2011; 130: 175–182. (12):2707–8]. Cancer 1999; 86: 826–835. 87. Buck K, Vrieling A, Zaineddin AK et al. Serum enterolactone and prognosis of 63. Jain M, Miller AB. Tumor characteristics and survival of breast cancer patients in postmenopausal breast cancer. J Clin Oncol 2011; 29: 3730–3738. relation to premorbid diet and body size. Breast Cancer Res Treat 1997; 42: 88. Clough-Gorr KM, Ganz PA, Silliman RA. Older breast cancer survivors: factors 43–55. associated with self-reported symptoms of persistent lymphedema over 7 years 64. Jung SY, Sereika SM, Linkov F et al. The effect of delays in treatment for breast of follow-up. Breast J 2010; 16: 147–155. cancer metastasis on survival. Breast Cancer Res Treat 2011; 130: 953–964. 89. Conroy SM, Maskarinec G, Wilkens LR et al. Obesity and breast cancer survival in 65. Maehle BO, Tretli S. Pre-morbid body-mass-index in breast cancer: reversed ethnically diverse postmenopausal women: the Multiethnic Cohort Study. Breast effect on survival in hormone receptor negative patients. Breast Cancer Res Treat Cancer Res Treat 2011; 129: 565–574. 1996; 41: 123–130. 90. Katoh A, Watzlaf VJ, D’Amico F. An examination of obesity and breast cancer 66. Shu XO, Zheng Y, Cai H et al. Soy food intake and breast cancer survival. JAMA survival in post-menopausal women. Br J Cancer 1994; 70: 928–933. 2009; 302: 2437–2443. 91. Rosenberg L, Czene K, Hall P. Obesity and poor breast cancer prognosis: an 67. Sparano JA, Wang M, Zhao F et al. Race and hormone receptor-positive breast illusion because of hormone replacement therapy? Br J Cancer 2009; 100: cancer outcomes in a randomized chemotherapy trial. J Natl Cancer Inst 2012; 1486–1491. 104: 406–414. 92. Schairer C, Gail M, Byrne C et al. Estrogen replacement therapy and breast 68. Vatten LJ, Foss OP, Kvinnsland S. Overall survival of breast cancer patients in cancer survival in a large screening study. J Natl Cancer Inst 1999; 91: relation to preclinically determined total serum cholesterol, body mass index, 264–270. height and cigarette smoking: a population-based study. Eur J Cancer 1991; 27: 93. Zhang S, Folsom AR, Sellers TA et al. Better breast cancer survival for 641–646. postmenopausal women who are less overweight and eat less fat. The Iowa 69. Allemani C, Berrino F, Krogh V et al. Do pre-diagnostic drinking habits influence Women’s Health Study. Cancer 1995; 76: 275–283. breast cancer survival? Tumori 2011; 97: 142–148. 94. Pfeiler G, Stoger H, Dubsky P et al. Efficacy of tamoxifen+/-aminoglutethimide in 70. Gregorio DI, Emrich LJ, Graham S et al. Dietary fat consumption and survival normal weight and overweight postmenopausal patients with hormone receptor- among women with breast cancer. J Natl Cancer Inst 1985; 75: 37–41. positive breast cancer: an analysis of 1509 patients of the ABCSG-06 trial. Br J Cancer 2013; 108: 1408–1414. 71. Kyogoku S, Hirohata T, Takeshita S et al. Survival of breast-cancer patients and body size indicators. Int J Cancer 1990; 46: 824–831. 95. Loi S, Milne RL, Friedlander ML et al. Obesity and outcomes in premenopausal and postmenopausal breast cancer. Cancer Epidemiol Biomarkers Prev 2005; 72. Mohle-Boetani JC, Grosser S, Whittemore AS et al. Body size, reproductive 14: 1686–1691. factors, and breast cancer survival. Prev Med 1988; 17: 634–642. 96. Mason BH, Holdaway IM, Stewart AW et al. Season of tumour detection 73. Suissa S, Pollak M, Spitzer WO et al. Body size and breast cancer prognosis: a influences factors predicting survival of patients with breast cancer. Breast statistical explanation of the discrepancies. Cancer Res 1989; 49: 3113–3116. Cancer Res Treat 1990; 15: 27–37. 74. Allin KH, Nordestgaard BG, Flyger H et al. Elevated pre-treatment levels of 97. Moon HG, Han W, Noh DY. Underweight and breast cancer recurrence and death: plasma C-reactive protein are associated with poor prognosis after breast cancer: a report from the Korean Breast Cancer Society. J Clin Oncol 2009; 27: a cohort study. Breast Cancer Res 2011; 13: R55. 5899–5905. 75. den Tonkelaar I, de WF, Seidell JC et al. Obesity and subcutaneous fat patterning 98. Lee K-H, Keam B, Im S-A et al. Body mass index is not associated with treatment in relation to survival of postmenopausal breast cancer patients participating in outcomes of breast cancer patients receiving neoadjuvant chemotherapy: Korean the DOM-project. Breast Cancer Res Treat 1995; 34: 129–137. data. J Breast Cancer 2012; 15: 427–433. 76. Eley JW, Hill HA, Chen VW et al. Racial differences in survival from breast cancer. 99. Chen X, Lu W, Zheng W et al. Obesity and weight change in relation to breast Results of the National Cancer Institute Black/White Cancer Survival Study. JAMA cancer survival. Breast Cancer Res Treat 2010; 122: 823–833. 1994; 272: 947–954. 100. Tao MH, Shu XO, Ruan ZX et al. Association of overweight with breast cancer 77. Gordon NH, Crowe JP, Brumberg DJ et al. Socioeconomic factors and race in survival. Am J Epidemiol 2006; 163: 101–107. breast cancer recurrence and survival. Am J Epidemiol 1992; 135: 609–618. Volume 25 | No. 10 | October 2014 doi:10.1093/annonc/mdu042 |  Annals of Oncology reviews 101. Hou G, Zhang S, Zhang X et al. Clinical pathological characteristics and 111. Ademuyiwa FO, Groman A, O’Connor T et al. Impact of body mass index on prognostic analysis of 1,013 breast cancer patients with diabetes. Breast Cancer clinical outcomes in triple-negative breast cancer. Cancer 2011; 117: Res Treat 2013; 137: 807–816. 4132–4140. 102. Kawai M, Minami Y, Nishino Y et al. Body mass index and survival after breast 112. Nichols HB, Trentham-Dietz A, Egan KM et al. Body mass index before and after cancer diagnosis in Japanese women. BMC Cancer 2012; 12: 149. breast cancer diagnosis: associations with all-cause, breast cancer, and cardiovascular disease mortality. Cancer Epidemiol Biomarkers Prev 2009; 18: 103. Labidi SI, Mrad K, Mezlini A et al. Inflammatory breast cancer in Tunisia in the era 1403–1409. of multimodality therapy. Ann Oncol 2008; 19: 473–480. 113. Dignam JJ, Wieand K, Johnson KA et al. Effects of obesity and race on prognosis 104. Berclaz G, Li S, Price KN et al. Body mass index as a prognostic feature in in lymph node-negative, estrogen receptor-negative breast cancer. Breast Cancer operable breast cancer: the International Breast Cancer Study Group experience. Res Treat 2006; 97: 245–254. Ann Oncol 2004; 15: 875–884. 114. Dignam JJ, Wieand K, Johnson KA et al. Obesity, tamoxifen use, and outcomes 105. Ewertz M, Gray KP, Regan MM et al. Obesity and risk of recurrence or death after in women with estrogen receptor-positive early-stage breast cancer. J Natl adjuvant endocrine therapy with letrozole or tamoxifen in the breast international Cancer Inst 2003; 95: 1467–1476. group 1–98 trial. J Clin Oncol 2012; 30: 3967–3975. 115. Majed B, Moreau T, Senouci K et al. Is obesity an independent prognosis factor in 106. Keegan TH, Milne RL, Andrulis IL et al. Past recreational physical activity, body woman breast cancer? Breast Cancer Res Treat 2008; 111: 329–342. size, and all-cause mortality following breast cancer diagnosis: results from the Breast Cancer Family Registry. Breast Cancer Res Treat 2010; 123: 531–542. 116. Dawood S, Broglio K, Gonzalez-Angulo AM et al. Prognostic value of body mass index in locally advanced breast cancer. Clin Cancer Res 2008; 14: 1718–1725. 107. von Drygalski A, Tran TB, Messer K et al. Obesity is an independent predictor of poor survival in metastatic breast cancer: retrospective analysis of a patient 117. Flatt SW, Thomson CA, Gold EB et al. Low to moderate alcohol intake is not cohort whose treatment included high-dose chemotherapy and autologous stem associated with increased mortality after breast cancer. Cancer Epidemiol cell support. Int J Breast Cancer 2011; 523276. doi:10.4061/2011/523276 Biomarkers Prev 2010; 19: 681–688. 108. Ewertz M, Jensen MB, Gunnarsdottir KA et al. Effect of obesity on prognosis after 118. Kwan ML, Chen WY, Kroenke CH et al. Pre-diagnosis body mass index and early-stage breast cancer. J Clin Oncol 2011; 29: 25–31. survival after breast cancer in the After Breast Cancer Pooling Project. Breast Cancer Res Treat 2012; 132: 729–739. 109. Tornberg S, Carstensen J. Serum beta-lipoprotein, serum cholesterol and Quetelet’s index as predictors for survival of breast cancer patients. Eur J Cancer 119. Rock CL, Doyle C, Demark-Wahnefried W et al. Nutrition and physical activity 1993; 29A: 2025–2030. guidelines for cancer survivors. CA Cancer J Clin 2012; 62: 243–274. 110. Newman SC, Lees AW, Jenkins HJ. The effect of body mass index and oestrogen 120. Griggs JJ, Mangu PB, Anderson H et al. Appropriate chemotherapy dosing for receptor level on survival of breast cancer patients. Int J Epidemiol 1997; 26: obese adult patients with cancer: American Society of Clinical Oncology clinical 484–490. practice guideline. J Clin Oncol 2012; 30: 1553–1561. Annals of Oncology 25: 1914–1918, 2014 doi:10.1093/annonc/mdu052 Published online 25 February 2014 Prediction of treatment-related toxicity and outcome with geriatric assessment in elderly patients with solid malignancies treated with chemotherapy: a systematic review 1 1 2 3 1 K. S. Versteeg , I. R. Konings , A. M. Lagaay , A. A. van de Loosdrecht & H. M. W. Verheul 1 2 3 Department of Medical Oncology, VU University Medical Center, Amsterdam; Department of Internal Medicine, Spaarne Hospital, Hoofddorp; Department of Hematology, VU University Medical Center, Amsterdam, The Netherlands Received 10 July 2013; revised 9 December 2013 & 3 February 2014; accepted 4 February 2014 Introduction: The number of older patients with cancer is increasing. Standard clinical evaluation of these patients may not be sufficient to determine individual treatment strategies and therefore Geriatric Assessment (GA) may be of clinical value. In this review, we summarize current literature that is available on GA in elderly patients with solid malignancies who receive chemotherapy. We focus on prediction of treatment toxicity, mortality and the role of GA in the decision-making process. *Correspondence to: Professor H. M. W. Verheul, Department of Medical Oncology, VU University Medical Center, Amsterdam, Netherlands. Kamer ZH 3A44, De Boelelaan 1117, 1081 HVAmsterdam, The Netherlands. Tel: +31-20-4444300; Fax: +31-20-4444079; E-mail: h.verheul@vumc.nl © The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Journal

Annals of OncologyPubmed Central

Published: Apr 27, 2014

There are no references for this article.